# Leveraging Contextual Information for Effective Entity Salience Detection

Rajarshi Bhowmik Marco Ponza Atharva Tendle Anant Gupta
Rebecca Jiang Xingyu Lu Qian Zhao Daniel Preoţiuc-Pietro
Bloomberg
{rbhowmik6<EMAIL_ADDRESS>Work was done while the author was
affiliated with Bloomberg

###### Abstract

In text documents such as news articles, the content and key events usually
revolve around a subset of all the entities mentioned in a document. These
entities, often deemed as salient entities, provide useful cues of the
aboutness of a document to a reader. Identifying the salience of entities was
found helpful in several downstream applications such as search, ranking, and
entity-centric summarization, among others. Prior work on salient entity
detection mainly focused on machine learning models that require heavy feature
engineering. We show that fine-tuning medium-sized language models with a
cross-encoder style architecture yields substantial performance gains over
feature engineering approaches. To this end, we conduct a comprehensive
benchmarking of four publicly available datasets using models representative
of the medium-sized pre-trained language model family. Additionally, we show
that zero-shot prompting of instruction-tuned language models yields inferior
results, indicating the task’s uniqueness and complexity.

Leveraging Contextual Information for Effective Entity Salience Detection

Rajarshi Bhowmik Marco Ponza Atharva Tendle Anant Gupta Rebecca Jiang
††thanks: Work was done while the author was affiliated with Bloomberg Xingyu
Lu Qian Zhao Daniel Preoţiuc-Pietro Bloomberg {rbhowmik6,
<EMAIL_ADDRESS>

##  1 Introduction

Many NLP studies have highlighted the importance of entities to understanding
the semantics of a document Wu et al. (2020b); Meij et al. (2012).
Automatically identifying entities in unstructured text documents and linking
them to an underlying knowledge base, such as Wikipedia, is one of the core
NLP tasks, with multiple shared tasks Tjong Kim Sang and De Meulder (2003);
Strauss et al. (2016), benchmarks Hoffart et al. (2011); Hovy et al. (2006);
Pradhan et al. (2013); Rijhwani and Preotiuc-Pietro (2020); Derczynski et al.
(2016), and studies Kolitsas et al. (2018); Nguyen et al. (2014) dedicated to
solving them.

Figure 1: An example of a document with salient and non-salient entities.
Entity mentions are highlighted in text.

Although an entity may play a crucial semantic role in document understanding,
not all entities in a text document play equal roles. Some entities are the
central subjects or actors within a document, around which the content and the
key events revolve. Others are mentioned only to provide additional context to
the main event. For example, some entities may be actors in peripheral events,
while others are deemed uninformative to the understanding of the document.
Thus, entity salience in a text is defined as a binary or ordinal rating to
quantify the extent to which a target entity is central to a given piece of
text Gamon et al. (2013); Dunietz and Gillick (2014). Figure 1 provides an
example text along with the mentioned entities and their salience. We note
that the salience of an entity to a text is independent of the user’s interest
when reading or searching the document Gamon et al. (2013), which is usually
referred to as entity relevance. It is also distinct from entity importance,
which quantifies the overall importance of the entity independent of the
document. Automatically inferring entity salience was shown to aid search
Gamon et al. (2013), improve ranking results Xiong et al. (2018), entity
detection Trani et al. (2018), and enable entity-centric applications such as
entity-centric summarization Maddela et al. (2022).

In this paper, we study the effectiveness of Transformer-based Pre-trained
Language Models (PLMs) in the task of entity salience detection. Prior work on
determining entity salience relied on heavy feature engineering to craft
features explicitly covering relevant aspects, such as entity frequency
Dunietz and Gillick (2014); Dojchinovski et al. (2016), position of entity
mentions within a document Dunietz and Gillick (2014); Trani et al. (2018),
relations to other entities Trani et al. (2018), document features, such as
its length Gamon et al. (2013) and lexical features, such as the name of the
entity or its context. Only a single recent work attempted to use PLMs in a
pipeline which included key entity detection, albeit the scope of the
evaluation was limited to a single high performing dataset Zhao et al. (2021).
In contrast, our proposed method uses a cross-encoder architecture where a
target entity’s name or alias and its contextual mentions in a text document
are encoded by a PLM encoder. The classifier uses the contextual
representation and, optionally, positional information about the entity
encoded through the decile position embedding vector of mentions to determine
the salience score of a target entity.

We conduct experiments on four publicly available datasets, two of which were
human annotated and two that were curated semi-automatically. We fine-tune
several cross-encoders using PLMs and demonstrate that these yield consistent
and significant improvements over feature-based methods, as well as prompting
instruction-tuned PLMs. The latter shows the novelty and complexity of the
task of entity salience detection, which requires the model to learn
significant task-specific semantic knowledge for this natural language
understanding task.

Our contributions in this paper are the following:

  * •

We propose a cross-encoder style architecture with explicit encoding of
position information for entity salience detection that shows consistent
improvements of 7 – 24.4 F1 score over previous feature engineering
approaches.

  * •

We establish a uniform benchmark of two human annotated and two semi-
automatically curated datasets for the task of entity salience detection that
we expect to be beneficial to future study of this task;

  * •

A faceted analysis of the models’ predictive behaviour.

##  2 Related Work

Understanding the aboutness of a document is one of the long-standing goals of
research in both Information Retrieval and Natural Language Processing Gamon
et al. (2013). Several types of approaches have been proposed, including
extracting key-terms Hulth (2003); Mihalcea and Tarau (2004), identifying
latent topics Blei et al. (2003), or generating text summaries Erkan and Radev
(2004). There has been a recent focus in using entities to understand the
content of the document. Towards this goal, the task of entity salience has
been first described for web pages in Gamon et al. (2013) and for news content
in Dunietz and Gillick (2014). This task can be viewed as a restricted form of
keyword or keyphrase extraction Alami Merrouni et al. (2020) if salience is
binary. For the rest of this study, we will use the concept of salience as
described in Gamon et al. (2013).

The salience labels for entities were obtained either by crowdsourcing labels
from multiple raters to identify salient entities Gamon et al. (2013);
Dojchinovski et al. (2016); Trani et al. (2018); Maddela et al. (2022) or by
using proxies. For example, Dunietz and Gillick (2014) hypothesize that
salient entities are those that appear in the article’s abstract. Wu et al.
(2020a) identifies an entity as salient if the Wikinews category that
corresponds to the entity is also labeled as the category of the article.

Past studies mostly proposed machine learning methods to infer the salience of
a given entity that relied on hand-crafted features. Features that can be
computed from the target entity mentions and document alone can be categorized
into the following: positional (e.g., position in the document, if entity is
in the abstract) Dunietz and Gillick (2014), count-based (e.g., number of
references to the entity) Dunietz and Gillick (2014); Wu et al. (2020a), local
context Trani et al. (2018), or global context Ponza et al. (2019). Further,
joint entity salience resolution can be performed by creating features using
the entity graph (e.g., centrality in the entity graph) Dunietz and Gillick
(2014); Trani et al. (2018). Finally, past work also showed that incorporating
external knowledge about entities from knowledge bases can boost predictive
performance Dojchinovski et al. (2016).

Automatically inferring salience for entities can directly benefit multiple
downstream applications, such as improving ranking results for queries
containing entities Xiong et al. (2018) or improving the performance of entity
detection by joint modelling Trani et al. (2018). Moreover, by inferring
salience, new entity-centric applications can be built, such as highlighting
salient entities in search Gamon et al. (2013), improving the interpretability
of news trends through salient entities Ponza et al. (2021), or identifying
entities for creating entity-centric summaries of news stories Maddela et al.
(2022); Hofmann-Coyle et al. (2022).

##  3 Problem Definition

We use the concept of salience as introduced in Gamon et al. (2013): salient
entities are entities explicitly mentioned in the document that are
objectively important as a function of the structure of the text.

The goal of the salience model is to produce a single salience score $\psi(e)$
for the entity $e$ using only the document $D$ and the explicit entity
mentions $\mathcal{M}_{e}$. We consider using external knowledge, such as
information about entities from knowledge bases, to be outside the scope and
leave integration of such knowledge for future work.

##  4 Methods

Pre-trained Language Models (PLMs) have shown a remarkable ability to encode
syntactic and semantic knowledge in their parameters Tenney et al. (2018,
2019) that can be leveraged when fine-tuned on downstream natural language
understanding (NLU) tasks. We postulate that PLMs can be harnessed to help in
entity salience detection, a target-based document-level NLU task. In this
section, we present an architecture based on the cross-encoder setup adapted
to the task of entity salience detection.

###  4.1 Cross-encoder

#### Encoding

Given a document $D$ and a target entity $e$, which is mentioned in the
document, we concatenate the target entity’s name and the document using a
special [SEP] token. We then encode the text using a Transformer-based pre-
trained encoder. Figure 2 shows the graphical representation of the cross-
encoder model. This setup allows the model to have deep cross attention
between the target entity and the entire document. Note that we do not
explicitly use the mention information $\mathcal{M}_{e}$ in this modeling
approach and rely on cross-attention to use mention information implicitly.

Figure 2: Graphical representation of the cross-encoder architecture w/ decile
position encoding.

#### Position Encoding

We compute the decile positions for each entity mention ($m\in M_{e}$) in the
document $D$ by taking a positional index $p_{m}\in\\{0,1,\dots,9\\}$,
indicating which part of document the mention belongs to if the document is
partitioned into $10$ equal chunks. Depending on the number and positions of
the mentions, the vector can contain multiple non-zero values in the $p$
vector. To obtain positional embeddings, we use an embedding layer that maps
positional indices to a dense vector of dimension $d_{model}$, formally
$\mathbf{h}_{pe}(m)=\texttt{Embedding}(p_{m})$.

#### Scoring

The output representation of the [CLS] token is concatenated with the mean
position embedding vector $\mathbf{h}_{pe}$ and fed to a scorer module that
produces a salience score $\psi(e)\in[0,1]$ for entity $e$. The salience
scorer is a feed-forward network with a sigmoid scoring function head.
Formally,

$\psi(e)=\sigma(\texttt{FFN}(\mathbf{h}_{\texttt{[CLS]}}||\mathbf{h}_{pe}))$

###  4.2 Optimization

We fine-tune the model described above by minimizing the binary cross entropy
loss that is calculated using the ground truth binary salience labels and the
predicted salience score $\psi(e)$.

##  5 Datasets

Dataset | NYT-Salience | WN-Salience | SEL | EntSUM
---|---|---|---|---
# Docs | 110,463 | 6,956 | 365 | 693
Doc Length (avg chars) | 5,079 | 2,106 | 1,660 | 4,995
# Unique entities | 179,341 | 23,205 | 6,779 | 7,854
# Mentions | 4,405,066 | 145,081 | 19,729 | 20,784
% Salient entities | 14% | 27% | 10% | 39%
Ground-truth | Abstract Alignment | Category Alignment | Human | Human

Table 1: Summary statistics and label collection methods for the datasets used
in our experiments.

In this section, we describe our entity salience benchmark, which consists of
four datasets: two datasets were curated using semi-automated methods and two
used human annotations. We provide summary statistics of these datasets and
label collection methods in Table 1.

#### NYT-Salience

This dataset is introduced in Dunietz and Gillick (2014) and is the largest
dataset to date for entity salience detection. The dataset is curated with an
assumption that salient entities are mentioned in the abstract of a news
article in the NYT Corpus Sandhaus (2008). Entities and their mentions are
identified using a classical NLP pipeline involving POS tagging, dependency
parsing, and noun phrase extraction. Despite being large-scale, the automatic
dataset creation process could introduce noise as corroborated by moderate
agreement numbers with human annotators on a subset of the data. The dataset
contains a binary salience label for each entity.

#### WN-Salience

Introduced in Wu et al. (2020a), this is another automatically curated dataset
consisting of Wikinews articles. These are annotated with Wikinews categories
by their authors. WN-Salience identifies salient entities by using the
hypothesis that an entity is salient if the Wikinews category that corresponds
to the entity is also labeled as a category of the article. Similar to NYT-
Salience, this dataset has binary salience labels.

#### SEL

This is another dataset based on Wikinews released by Trani et al. (2018).
However, unlike WN-Salience, this dataset is human annotated, where multiple
human annotators ranked the salience of entities into one of four categories.
To conform with the binary labels of the other datasets, we map the 4
categories into binary labels of $\\{0,1\\}$ by mapping the bottom two classes
to not salient and the top two classes to salient.

#### EntSUM

This dataset was introduced in Maddela et al. (2022). To construct this
dataset, a randomly selected set of entities spanning a subset of $693$
articles from the NYT corpus were assigned salience labels by human annotators
on a four-point scale, ranging between $[0,3]$. For each document entity pair,
two independent annotations were collected, which were increased up to $5$ in
case of disagreements. If the average annotation score is greater than $1.5$
for an entity, it is assigned a positive salience label.

###  5.1 Data Enrichment with Inferred Mentions

Except for EntSUM, the other datasets do not have explicit entity mention
offsets as annotations, which are necessary for many feature-based approaches
and to compute positional embeddings. While SEL contains only the mention
surface texts per entity, NYT-Salience and WN-Salience only provide the start
and end character indices (aka mention offsets) of the very first mention of
an entity. To this end, we infer additional mentions of an entity within the
text using a combination of Flair NER Akbik et al. (2019) and pattern
matching.

For SEL, since the mentions are available, we use a pattern matching approach
to match the surface text of the mentions to infer mention offsets. For NYT-
Salience and WN-Salience, we first use Flair NER to identify mentions of named
entities in the text. We attempt to match these mentions to the first mention
of each entity in the document provided in the respective datasets. Since the
surface text of other mentions may differ from the first mention, we
additionally use the overlap between a mention’s surface text and the entity
name as a candidate mention for that entity. Applying this approach, we infer
additional mentions of an entity in the text and their offsets. While this
process could introduce some noise, the overall quality of the datasets are
enhanced through this process.

##  6 Experiments

We experiment on our entity salience benchmark with our proposed PLM-based
method, other ML and heuristic-based approaches used in past research, as well
as an instruction-tuned PLM.

###  6.1 Data Splits

Prior works Dunietz and Gillick (2014); Trani et al. (2018); Wu et al. (2020a)
use inconsistent (or not reported) train/validation/test splits. NYT-Salience
and WN-Salience datasets are provided with train/test splits (but no
validation), whereas SEL dataset is provided without any splits. This makes it
hard to benchmark previous works with a fair comparison across models. To
overcome this issue, we do a temporal split of NYT-Salience’s and WN-
Salience’s original training sets into a new train/validation sets based on
the publication time of the news stories, which provides a more realistic
testing setup Huang and Paul (2018); Rijhwani and Preotiuc-Pietro (2020). We
also perform a temporal split of SEL and EntSUM datasets into
train/validation/test sets.

Source | Type | Method | NYT-Salience | WN-Salience
---|---|---|---|---
P | R | F1 | P | R | F1
Dunietz and Gillick (2014) | Heuristic | First Sentence | 59.5 | 37.8 | 46.2 | – | – | –
Dunietz and Gillick (2014) | ML | Position & Frequency | 59.3 | 61.3 | 60.3 | – | – | –
Dunietz and Gillick (2014) | ML | All Features | 60.5 | 63.5 | 62.0 | – | – | –
Ponza et al. (2019) | ML | SWAT | 62.4 | 66.0 | 64.1 | – | – | –
Wu et al. (2020a) | Heuristic | First Sentence | 56.0 | 41.0 | 47.3 | 47.9 | 53.2 | 50.4
Wu et al. (2020a) | ML | Positional Feature | 19.0 | 41.3 | 26.0 | 29.1 | 78.9 | 42.5
Wu et al. (2020a) | ML | Features & GBDT | 39.2 | 59.7 | 47.3 | 29.2 | 48.1 | 36.3
Our Implementations | Heuristic | Positional Headline | 57.5 | 42.0 | 48.5 | 46.1 | 51.5 | 48.7
Heuristic | Positional Headline & Lead | 49.8 | 55.4 | 52.5 | 41.0 | 60.0 | 48.7
Heuristic | Entity Frequency | 53.7 | 53.3 | 53.6 | 37.3 | 61.9 | 46.6
ML | Features & GBDT | 61.0 | 57.4 | 59.2 | 46.2 | 53.3 | 49.5
PLM (RoBERTa) | Target Entity Masking | 64.6 | 50.2 | 56.5 | 57.0 | 65.4 | 60.9
PLM (Flan-T5) | Zero-shot prompting | 43.0 | 60.6 | 50.3 | 38.8 | 53.2 | 44.9
Our Models | PLM (RoBERTa) | cross-encoder | 75.9 | 87.1 | 81.1 | 71.8 | 73.6 | 72.7
PLM (DeBERTa) | cross-encoder | 77.5 | 87.4 | 82.1 | 71.5 | 78.3 | 74.8
PLM (RoBERTa) | cross-encoder w/ position emb. | 78.7 | 84.2 | 81.4 | 71.2 | 76.7 | 73.8
PLM (DeBERTa) | cross-encoder w/ position emb. | 75.9 | 88.4 | 81.7 | 73.3 | 76.1 | 74.7

Table 2: Results on the NYT-Salience and WN-Salience datasets. The ground-
truth of these datasets was generated via abstract/category alignment. The top
section presents results as originally reported in the source papers.

Source | Type | Method | SEL | EntSUM
---|---|---|---|---
P | R | F1 | P | R | F1
Trani et al. (2018) | ML | SEL (w/ 5-fold cross val.) | 50.0 | 61.0 | 52.0 | – | – | –
Ponza et al. (2019) | ML | SWAT (w/ 5-fold cross val.) | 58.0 | 64.9 | 61.2 | – | – | –
Our Implementations | Heuristic | Positional Headline | 26.6 | 78.4 | 39.7 | 60.7 | 18.5 | 28.4
Heuristic | Positional Headline & Lead | 22.1 | 87.1 | 35.3 | 51.2 | 31.6 | 39.1
Heuristic | Entity Frequency | 13.5 | 57.8 | 21.9 | 48.4 | 54.0 | 51.0
ML | Features & GBDT | 26.6 | 78.4 | 39.7 | 60.7 | 52.0 | 56.0
ML | SEL GBDT | 71.1 | 47.8 | 57.1 | – | – | –
PLM (RoBERTa) | Target Entity Masking | 36.3 | 13.8 | 20.0 | 63.0 | 41.7 | 50.2
PLM (Flan-T5) | Zero-shot prompting | 27.0 | 81.7 | 40.6 | 50.7 | 54.5 | 52.5
Our Models | PLM (RoBERTa) | cross-encoder | 51.6 | 73.6 | 60.6 | 65.5 | 60.6 | 63.0
PLM (DeBERTa) | cross-encoder | 64.1 | 73.6 | 68.5 | 64.9 | 59.2 | 61.9
PLM (RoBERTa) | cross-encoder w/ position emb. | 63.0 | 69.9 | 66.3 | 67.5 | 57.0 | 61.8
PLM (DeBERTa) | cross-encoder w/ position emb. | 67.3 | 62.4 | 64.7 | 72.1 | 51.5 | 60.1

Table 3: Results on the SEL and EntSUM datasets. The ground-truth of these
datasets was generated via human annotation. The top section presents results
as originally reported in the source papers.

###  6.2 Baselines

First, we list all methods used in past research, for which we report the
results from the original papers.

  * •

First Sentence. Classifies an entity as salient if it appears in the first
sentence of the document’s body; used in both Dunietz and Gillick (2014) and
Wu et al. (2020a).

  * •

Position & Frequency Dunietz and Gillick (2014). Feeds the first sentence
index and the frequency features of an entity into a logistic regression
model.

  * •

All Features Dunietz and Gillick (2014). Uses a series of features based on
position, frequency, and PageRank signals fed into a logistic regression
model.

  * •

SEL Trani et al. (2018). Uses a combination of features based on position,
frequency, and Wikipedia graph statistics fed into a Gradient Boosted Decision
Tree algorithm implemented in sklearn Pedregosa et al. (2011).

  * •

SWAT Ponza et al. (2019). Uses a set of features similar to the SEL Method
described above, with the addition of features based on entity embeddings. All
features are fed into a Gradient Boosted Decision Tree algorithm implemented
in XGBoost Chen et al. (2015).

  * •

Positional Feature Wu et al. (2020a). Uses the index of the first sentence the
entity is mentioned as a feature in a logistic regression model. This method
provides best results on the WN Salience dataset in Wu et al. (2020a).

Next, we re-implement a set of common methods based on the above baselines in
order to be able to test them on all four datasets. This ensures the
evaluation is performed on the same experimental setup.

  * •

Positional Headline. Classifies an entity as salient whether it appears in the
headline of the input document.

  * •

Positional Headline & Lead. Classifies an entity as salient if it appears in
the headline of the document or in the first sentence (lead sentence) of the
document.

  * •

Entity Frequency. Classifies an entity as salient if they are more frequent
than a given value. For each dataset, we calculated different thresholds and
reported the best results. Thresholds can be found in the Appendix.

  * •

Features & GBDT. This method uses the most common features from past works
Dunietz and Gillick (2014); Wu et al. (2020a); Trani et al. (2018); Ponza et
al. (2019) — i.e., entity’s first sentence index, and entity frequency — and
feeds them into a GBDT model implemented using LightGBM Ke et al. (2017).

  * •

SEL GBDT. Follows the method from Trani et al. (2018) and uses sklearn’s GBDT
Pedregosa et al. (2011) to train a model on the features provided with the SEL
dataset.

  * •

Target entity masking. This method feeds the input to a Transformer-based
encoder (RoBERTa-base) with the target entity mentions represented through a
special mask token. The salience prediction is obtained by mean pooling the
mask token representations and passing this through a feed-forward network.

  * •

Zero-shot prompting. We test an instruction-tuned PLM Wei et al. (2021) by
zero-shot prompting. The prompt introduces the task description followed by
the input text and a target entity and formulates the output as a yes/no
question. The PLM, already instruction-tuned on a large collection of NLU
tasks, attempts to provide an answer based on the prompt, input text and
target entity. This family of models has been demonstrated to be robust and
versatile on multiple benchmarks Wei et al. (2021). We use Flan-T5-Large Chung
et al. (2022), which is comparable in size to other PLMs we use as the base
models for the cross-encoders.

###  6.3 Experimental Setup

We use RoBERTa-base Liu et al. (2019) and DeBERTa-v3-base He et al. (2023) as
the base PLM for experiments. For each of these base models, we train both a
cross-encoder model and a cross-encoder model augmented with decile positional
embeddings.

For training our proposed models, we use AdamW Loshchilov and Hutter (2019) as
the optimizer. We perform a hyperparameter search for learning rate using the
following set of values: $\\{0.001,0.0005,0.0002,0.0001,0.00005\\}$. We train
our models for a maximum of $10$ epochs with early stopping based on the
validation set performance. We pick the best performing model checkpoints for
each dataset based on the performance on the validation set. In Tables 2 and
3, we report the performance of our models and the baselines using the
standard classification metrics (i.e., Precision, Recall, and F1) on the
positive (salient) class, following previous research on entity salience.

For training and inference of each Transformer-based model, we use a single
NVIDIA V100 GPU with 32GiB GPU memory, 4 CPUs, and 128 GiB of main memory.

###  6.4 Results

In Tables 2 and 3, we present the experimental results of the baselines and
our proposed models on the four datasets described in Section 5.

#### Comparison with feature-based methods.

We observe that the cross-encoder model significantly outperforms all baseline
models in F1 score. It also yields better precision compared to the baselines
for three of the four datasets. Only for the SEL dataset does the SEL GBDT
model trained on publicly available pre-computed features produce a model with
better precision than the cross-encoder. We observe that adding the decile
positional embedding with cross-encoder improves the precision across all
datasets, but also degrades the recall in every dataset except NYT-Salience.

The Target Entity Masking approach, which also leverages contextual
information with a transformer-based model yields mixed results. Overall, the
model is able to obtain better precision than the feature-based models for all
datasets except SEL, but the model suffers from poor recall across all
datasets, resulting in significantly worse F1 scores especially when compared
to cross-encoder models.

Our re-implementation of positional methods and GBDT methods are consistent
with the performance reported in prior works. The variance in numbers can be
attributed to the enrichment of datasets with inferred mentions (Section 5.1)
and the explicit train/dev/test data split used in our experiments (Section
6.1).

#### Comparison with zero-shot prompting of a large language model.

To the best of our knowledge, this is the first evaluation of zero-shot
prompting of instruction-tuned models (here Flan-T5-Large) for the entity
salience detection task. We observe that the model is able to provide
comparable or better performance with the heuristic-based methods, with the
exception of the WN-Salience dataset. However, it falls short of the dedicated
cross-encoder model by double digit margins, showing that this is a
distinctive task and requires a dedicated model. We further discuss causes for
this performance in the Appendix (Section 9.1), along with the implementation
details.

##  7 Analysis

Model | NYT-Salience | WN-Salience | SEL | EntSUM
---|---|---|---|---
P | R | F1 | P | R | F1 | P | R | F1 | P | R | F1
Cross-encoder w/ first mention | 54.2 | 57.5 | 55.8 | 69.6 | 80.4 | 74.6 | 59.8 | 76.1 | 67.0 | 69.1 | 53.2 | 60.2
Cross-encoder w/ all mentions | 77.5 | 84.4 | 82.1 | 71.5 | 78.3 | 74.8 | 64.1 | 73.6 | 68.5 | 64.9 | 59.2 | 61.9

Table 4: Performance comparison of cross-encoder models with only the first
mention vs. all inferred mentions.

In this section, we perform an analysis of model predictions in order to gain
more insights into model behavior and understand potential avenues for further
improvement. We thus break down performance by different factors including:
the importance of inferring all entity mentions, the position of the first
entity mention, and entity mention frequency.

(a) Performance with respect to the position of the mentions. There are no
mentions outside of the context window for NYT.

(b) Performance with respect to the frequency of the entities. The test split
of SEL dataset does not contain any entity with more than 10 mentions in a
document.

Figure 3: Stratified analysis across models and datasets.

###  7.1 Impact of Inferred Mentions

In Section 5.1, we inferred additional mentions of an entity for the NYT-
Salience and WN-Salience datasets. We compare the performance of our best
model that leverages multiple mentions of an entity to its version trained
with only the first mentions of entities in a document. The results in Table 4
show that doing so consistently improves the performance of our models across
all datasets. In particular, for the largest dataset, NYT-Salience, our model
achieves a substantial gain of 27.3 F1 points. This experiment showcases the
importance of augmenting our datasets with additional mentions and the
importance of explicitly modelling contextual information present around all
entity mentions.

###  7.2 Stratified Analysis on First Mention Position

We compare our cross-encoder models against the Features & GBDT model, our re-
implemented baseline that relies on the most popular features used in prior
works Dunietz and Gillick (2014); Wu et al. (2020a); Trani et al. (2018). As
shown in the results from Tables 2 and 3, among other features, positional
features are most informative for salience. Intuitively, if an entity is
mentioned in the headline or in the first sentence of a news article, there is
high probability of that entity being salient.

Figure 3 shows that all models perform well when the first mention falls in
the headline or the first sentence of the document. We notice that the cross-
encoder models constantly outperform the Features & GBDT model and the largest
gains are observed in the SEL and WN-Salience datasets. This observation
indicates that the cross-encoder models are able to use the context to
identify that mentions that occur in the headline or the first parts of the
document are often salient without explicitly using this information as a
feature.

We also investigate the performance of the models when the first mention falls
inside or outside the context window of the PLM (here, 512 tokens). When
mentions fall inside the context window, we observe that the cross-encoder
models consistently outperform the Features & GBDT model. When the mention
falls outside the context window, the model predictions become close to
random, which is expected, as the model does not have immediate contextual
information around the mention. Using models that can deal with longer inputs
would be a promising direction for improvement for these samples Beltagy et
al. (2020). Interestingly, for WN-Salience, the Features & GBDT model also
performs considerably worse outside the first 512 tokens.

###  7.3 Stratified Analysis on Mention Frequency

Similar to mention position analysis, we compare our cross-encoder models
against the Features & GBDT model, which uses mention frequency as one of its
input features. Figure 3 shows how the cross-encoder models and Features &
GBDT compare with varying frequency of entity mentions.

For salient entities with single mentions, the cross-encoder model performs
significantly better than the Features & GBDT model. In particular, for the
NYT-Salience dataset, the Features & GBDT model fails to predict any of the
single mention entities as salient. This observation indicates that the cross-
encoder models do not simply model the mention frequency, but potentially
leverage other contextual information to determine the salience of entities
with a single mention.

The performance of the Features & GBDT model improves with more mentions per
entity. In fact, for the frequency range of 6-10 mentions per entity, the
Features & GBDT model performs better than the cross-encoder models for EntSUM
and SEL datasets. This observation indicates the over-reliance of the Features
& GBDT model on mention frequency to determine salience, but also that the
cross-encoder cannot fully use this heuristic.

##  8 Conclusion

This paper aims to leverage the semantic knowledge encoded in pre-trained
language models for entity salience detection. We propose the cross-encoder
method based on Transformer-based PLMs with positional representations and
compare its performance to several ML-based, heuristic methods and
instruction-tuned PLMs across four different datasets, two human-annotated and
two automatically curated. Across all our experiments, the cross-encoder model
based on pre-trained language models outperforms all other methods, often with
double digit gains in F-1 score. Analyses of model behavior illustrate the
important effects of mention frequency, mention position, and document length
on performance, highlighting areas of future work.

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##  9 Appendix

###  9.1 Implementation details of zero-shot prompting of instruction-tuned
Flan-T5-Large

We define the problem of salience detection from the perspective of zero-shot
prompting as follows: given a definition of entity salience and document text,
ask the model to generate a "yes" or a "no" if a particular entity is salient
or not. We experimented with various PLMs available on Hugging
Face,111www.huggingface.com instruction prompts, and text generation
parameters in the Hugging Face API to arrive at the best performance within
our defined constraints of using an open source PLM with a comparable size to
other PLMs in our work.

Figure 4 shows the prompt we used and Table 5 lists the generation parameters.
We speculate the following causes for the relatively lower precision obtained
using this method:

  * •

The instruction defines the salience task definition, but doesn’t provide any
reference examples (few-shot prompting) to align the definition of salience.
This leads to the model identifying an entity as salient based on its
frequency in the document. Creating a few-shot prompt is challenging as we
need to limit the maximum input length of the prompt to prevent out-of-memory
issues

  * •

We truncate the document text so that the entire prompt is 512 tokens or less,
thus throwing away information on the document.

### Instruction ###
An entity is considered salient in a text if the text is about that entity.
Classify if a given entity is salient in the below text or not:
Text: {text}
Entity: {entity}
Question: Is the above Entity salient in the above Text? (yes/no):
Answer: Figure 4: Instruction for zero-shot prompting Generation parameter | Value
---|---
top_k | 0
top_p | 0.9
temperature | 0.9
max_new_tokens | 128
Table 5: Parameters for generating zero-shot prompt Figure 5: Performance of
the Entity Frequency baseline over different thresholds.

###  9.2 Thresholds for Entity Frequency baseline

Figure 5 shows the performance of the Entity Frequency baseline by varying the
minimum number of times an entity has to occur in the input document to be
classified as salient.

Figure 6: Graphical representation of the Target Entity Masking architecture.


# Study of Exospheric Neutral Composition of Mars observed from Indian Mars
Orbiter Mission

Kamsali Nagaraja  Praveen Kumar Basuvaraj  Department of Physics, Bangalore
University, Bengaluru 560056 India  S. C. Chakravarty  Department of Physics,
Bangalore University, Bengaluru 560056 India  Praveen Kumar K  Indian Space
Research Organisation Headquarters, Bengaluru 560231 India

###### Abstract

The raw exospheric composition data of Mars for the period September 2014 to
October 2015 has been retrieved and analysed using the observations carried
out by Mars Exospheric Neutral Composition Analyser (MENCA) payload on-board
the Mars Orbiter Mission (MOM) launched by India on 5 November 2013. The state
parameters viz. latitude, longitude, altitude and solar zenith angle coverage
of the partial pressures of different exospheric constituents are determined
and assigned to enrich the usefulness of data with orbit-wise assimilation
particularly between 250 and 500 km altitude range of main interest. Apart
from getting the results on mean individual orbits’ partial pressure profiles
during the northern winter, the variations of total as well as partial
pressures are also studied with respect to the distribution of major
atmospheric constituents and their dependence on solar activity. In
particular, $CO_{2}$ and $O$ variations are considered together for any
differential effects due to photo-dissociation and photo-ionisation. The
results on gradual reduction in densities due to the decreasing daily mean
sunspot numbers and strong response of $CO_{2}$ and $O$ pressures to solar
energetic particle events like that of 24 December 2014 following a solar
flare were discussed in this paper.

Keywords: MOM; MENCA; Mars Exosphere; Solar Activity; Mass Spectrometer

##  1 Introduction

Early measurements of atmospheric densities and chemical composition close to
the surface of Mars were carried out using earth-based remote sensing
techniques of high resolution optical spectrometers incorporating the Doppler
shift method. These measurements in visible and IR bands indicated $CO_{2}$ as
the dominant constituent with the total atmospheric pressure of about
$\approx$7 mb which were later found to be corroborated with the Viking 1 and
2 lander spacecraft results (Moroz et al., 1998 and references there in).
Similarly from earth-based spectral line measurements, the Martian surface-air
concentrations of water vapour ($H_{2}O$), molecular oxygen ($O_{2}$), carbon
monoxide ($CO$) and isotope ratios of oxygen ($O$) and carbon ($C$) were
determined (Kaplan et al.,1969; Barker et al., 1972; Carleton and Traub, 1972;
Young and Young, 1977). During the initial years of space exploration, optical
spectrometers in UV and IR bands were used in e.g., Mariner 9 orbiter mission
resulting in the detection of hydrogen corona and the confirmation of small
densities of ozone ($O_{3}$) in the polar region of Mars (Lane et al., 1973;
Barth and Dick, 1974). There were similar observations of lower atmospheric
constituents from other spacecraft missions including Mars 3 & 5 and Mariner 6
& 7\. While the efforts to measure the atmospheric parameters including the
densities of the gaseous constituents near the surface of Mars in the pre-
Viking period i.e., before 1976, have provided some basic information, there
was a need to carry out further observations particularly related to the
spatial distribution of the atmospheric constituents in the thermosphere and
exosphere of Mars. Such an opportunity was provided by the launch of two
Viking spacecraft missions during September 1976 to carry out the observations
using mass spectrometers both on the entry probe as well as on the lander. The
first in-situ data of the Martian upper atmospheric composition, densities and
temperatures were obtained from this twin Viking mission (Hanson et al., 1977;
Nier and McElroy, 1977; Owen et al., 1977; Hanson and Mantas, 1988). This was
followed by Phobos 2 (1989), Mars Global Surveyer (MGS, 1997) and the Mars
Express (MEX, 2004) missions with main emphasis to study the interaction of
solar wind charged particles with Mars, global mapping of the magnetic field
and estimating escape rates of ions from Mars (Sagdeev and Zakharov, 1989;
Acuna et al., 1999; Barabash et al., 2007). The Mars Pathfinder mission (1997)
was the first successful lander-rover mission with similar findings as that of
Viking (Magalhaes et al., 1999). Mars Odyssey (MO, 2002) and Mars Exploration
Rovers Spirit and Opportunity (MER, 2004) searched for the surface features
affected by flow of water and the atmosphere close to the ground (Mangold et
al., 2004; Squyres et al., 2006). More recent missions like Mars
Reconnaissance Orbiter (MRO, 2006), Phoenix lander (2008) and Mars Science
Laboratory (MSL, 2012) revealed hemispherical differences in the variation of
precipitable water (Smith et al., 2009), role of recent degassing due to
volcanic activity in the evolution of modern atmosphere of Mars (Niles et al.,
2010) and enhanced $D/H$ (Deuterium/Hydrogen) ratio in the clay minerals
pointing to a longer history of hydrogen escape and hence water (Mahaffy et
al., 2015). The quadrupole mass spectrometer (QMS) as part of the instrument
suite on the MSL’s Curiosity rover measured the chemical concentrations and
isotopic fractions of volatile compounds. The surface atmospheric composition
of Mars consists of about 96% carbon dioxide ($CO_{2}$) and small
concentrations of nitrogen ($N_{2}$), argon ($Ar$), and trace amounts of
oxygen ($O_{2}$), Water vapour ($H_{2}O$) and ozone ($O_{3}$) (Mahaffy et al.,
2013). The complete set of near surface meteorological data obtained from
Viking landers (1976) to the Curiosity rover (2012) have been analysed and
results have been consolidated in terms of diurnal, seasonal and inter-annual
variations of meteorological parameters including dust storms over a span of
more than 20 Martian years (Martinez et al., 2017). A similar analysis to
characterise the thermosphere-exosphere system has not been possible due to
paucity of observational data building up time sequences of the altitude and
latitudinal profiles of the meteorological parameters and neutral/ionised gas
concentrations (Bougher et al., 2014).

Recently the Indian Mars Orbiter Mission (MOM, 2014) and the Mars Atmosphere
and Volatile Evolution (MAVEN, 2014) from NASA have been launched for the
study pertaining to the evolution and escape of atmospheric/ionospheric
constituents. MAVEN makes additional observations of the constituents in the
fringe of the mesosphere. It is an important point to note that till September
2014 when MOM and MAVEN arrived at Mars, the data from the Viking mission
remained the only in-situ measurements of neutral and ion composition of the
thermosphere and its fringe region just touching the exosphere of Mars. The
main purpose of this paper is to study the spatial and temporal distribution
of atmospheric composition of the thermosphere-exosphere region of Mars which
is essential to understand the diurnal, latitudinal, seasonal and solar
activity driven variations and the escape of neutral/ion atmospheric species
from Mars. The in-situ observations carried out by MENCA (Mars Exospheric
Neutral Composition Analyser) and NGIMS (Neutral Gas and Ion Mass
Spectrometer) on board MOM and MAVEN orbiter missions have opened up new
vistas to further characterise the thermosphere-exosphere of Mars. The primary
objective is therefore to reduce the gap in knowledge about the variations of
atmospheric constituents in the exobase to exosphere of Mars using recent data
from MENCA.

Figure 1: Neutral composition number density profiles in the thermosphere and
exobase regions of Mars as measured by Viking 1 lander (Nier and McElroy,
1977). The lander covered SZA of 41∘-44∘ and ground co-ordinates in latitude
and longitude of 14∘-17∘ and 302∘-306∘ respectively.

##  2 Early measurements of the upper atmosphere of Mars

The first in situ observations providing the vertical profiles of neutral
gases, ion, electron densities and temperatures of the atmosphere of Mars were
carried out by the Viking 1 and 2 landers when these descended down to the
surface of Mars.

The neutral mass spectrometric data on the composition of the upper atmosphere
of Mars indicates that the main constituents of the Martian atmosphere are
carbon dioxide ($CO_{2}$), Argon ($Ar$), molecular nitrogen ($N_{2}$), carbon
monoxide ($CO$), and with photo-chemically produced atomic oxygen ($O$) (Nier
and McElroy, 1977). The surface level volume mixing ratios of $CO_{2}$, $Ar$
and $N_{2}$ are 0.960, 0.0193 and 0.0189 respectively (Mahaffy et al., 2013).
The altitude variations of some important atmospheric constituents above the
well mixed atmosphere, i.e., above the mesosphere and into the thermosphere,
where the diffusion of individual atmospheric species dominate in comparison
to turbulent mixing (Haberle et al., 2002), are shown in Figure 1 as an
example taken from Viking results (Nier and McElroy, 1977).

During the period of atmospheric observation while in descent from 200 km, the
lander covered SZA of 41∘-44∘ and ground coordinates in latitude and longitude
of 14∘-17∘ and 302∘-306∘ respectively (Withers and Lorenz, 2002). So the data
on altitude variation of the gaseous constituents shown in Figure 1 are taken
under similar solar radiation condition and within a marginal variation of 4
degrees of latitude and longitude. These results were later extended from 200
to 300 km through scale height extrapolations (Hanson et al., 1977). After the
operation of Viking, a number of orbiter, lander and rover missions have
enriched the information about Mars’s atmospheric phenomena mainly in its
meteorological context. Based on theoretical considerations, Mars atmospheric
general circulation models such as Mars Climate Database (MCD) and Mars
Thermospheric General Circulation Model (MT-GCM) have been developed extending
into the exosphere but validated mainly using the in-situ observations from
Viking (Lewis et al., 1999; Bougher et al., 2012). Another advantage of
MOM/MAVEN data is that it helps determining the solar activity effects as the
observations have been taken during the period of moderately high solar
activity condition as compared to Viking probe measurements.

The present work therefore involves conversion of the available near-raw data
of MENCA experiment into a calibrated data set with associated tags of time,
altitude, latitude, longitude and solar zenith angles for detailed analysis to
derive the exospheric composition and its variability.

##  3 Observation and Data Analysis

The Indian Mars Orbiter Mission (MOM) was launched on 5 November 2013. The
probe arrived in Mars on 24 September 2014 into an highly eccentric orbit of
422 km x 76,993 km with an orbital period of $\approx$ 67 hours. Later
manoeuvres during December 2014 brought down the periareion altitude to around
262 km (Bhardwaj et al., 2016). One of the payloads of MOM is MENCA for the
measurement of total atmospheric pressure and partial pressures of various
atmospheric constituents covering 1-300 amu (Bhardwaj et al., 2015). The data
sets used for this paper are obtained when MENCA was operated in the mass
range of 1-100 amu. Raw data of MENCA payload were made to public for the
period 24 September 2014 to 23 September 2017 through the ISRO Space Science
Data Center (ISSDC). This archived data consists of total and partial pressure
values in the units of Torr with time resolution of 12 to 30 seconds near
periareion. Before this data can be used for scientific studies, it needs
further processing in terms of calibration factors and ancillary data like
latitude, longitude, altitude and solar zenith angle.

Table 1: Data availability sample; giving MOM orbit number and number of data files along with their time coverage Orbit # | Observation start time | Observation end time | Total files
---|---|---|---
0031 | 2014-10-17 11:01:17 | 2014-10-19 18:43:23 | 28
0032 | 2014-10-20 02:01:18 | 2014-10-21 06:38:39 | 10
0034 | 2014-10-28 05:41:41 | 2014-10-30 20:32:55 | 16
0035 | 2014-11-01 23:16:21 | 2014-11-02 14:01:41 | 04
0036 | 2014-11-02 16:26:04 | 2014-11-05 07:11:51 | 16
0038 | 2014-11-05 10:13:05 | 2014-11-08 00:40:58 | 10
0039 | 2014-11-08 03:34:50 | 2014-11-10 18:08:43 | 12
0040 | 2014-11-11 19:03:42 | 2014-11-13 11:57:10 | 10

The data is identified with respect to different orbit numbers of MOM for
aforementioned period covering 88 orbits. The raw data of each orbit is
included in a number of file pairs, each pair containing: (a) total
atmospheric pressure (in Torr) and (b) partial pressures (in Torr) of the
constituents for different time intervals. The individual files for partial
pressures contain the data in a continuous time sequenced array form, which is
converted into tabulated columnar format by marking each column for: (i) time
of observation in UTC, (ii) corresponding 9-values of partial pressures for
each constituent with amu between 1-100 and (iii) total pressures time
synchronised with partial pressure measurement times using the second file in
the pair. Table 1 shows an example of information on number of files
containing data in a streaming time sequence mode for different orbits. ISSDC
has also provided the specific SPICE (Spacecraft Planet Instrument C-matrix
Events) kernel files for one year period of observations consistent with the
internationally standardized service modules (Acton C.H. et al., 1996; 2017)
required to compute the altitude, latitude, longitude and solar zenith angle
values tagged to each time of observation so as to characterize and tabulate
the pressure data with respect to these localization parameters.

Further data processing involves: (a) combining each pair of file to convert
into one file having the partial pressures (maximum pressure value out of
9-values of observation for each of the 100 amu bins) as well as the time
synchronised total pressure. Thus 100 maximum pressure values are selected for
each amu from 1-100 for the same time epoch constituting a single record for
further analysis, (b) computing the altitude, latitude, longitude and solar
zenith angle for each time epoch of observation using the kernels of SPICE
system mentioned earlier and (c) conversion of raw partial pressure data using
calibration information provided along with the science data for different amu
values. The final calibrated partial pressure values along with the associated
ephemeral and spatial information for each orbit is used for further analysis
and scientific studies of the atmospheric constituents of Mars. After the
above treatment of data, the MOM orbit-wise analysis is carried out to select
the useful partial pressure values from the lowest altitude to about 500 km.
This height limit is decided based on the requirement of our present study.
The sum of the partial pressures of major gas constituents is also used to
compute the total pressures for studying relative variation with time,
altitude, etc. This method is found useful when the concentrations of certain
constituents like water vapour, molecular and atomic hydrogen etc. show
abnormally high values due to out-gassing. Such contamination due to out-
gassing has been reported by the payload team (Bhardwaj et al., 2017).
Statistical analysis is carried out for deriving the monthly mean values and
standard errors at 95% confidence interval.

##  4 Results

The MENCA in-orbit payload operation plan was governed by the science
objective to study the atmospheric composition in the exosphere which has been
constrained by the variation of periareion (260-430 km) for the first year
(September 2014 to October 2015) of observation. However this periareion
coverage of lowest altitudes is best suited to meet the scientific objectives
compared to the situation of subsequent observations.

Figure 2: Periareion altitude coverage of MENCA observations during October
2014 to October 2015

Figure 3: MOM trajectory (dotted) and MENCA observations (coloured) in each
orbit during Dec 2014

Figure 2 shows this variation of the altitude of the periareion of MOM
spacecraft during the first year of data collection. From 27 May 2015 to 01
July 2015, MOM was behind the Sun as viewed from the Earth and hence no
observations were made during the communication black-out period. During each
MOM orbit, the MENCA payload has been operated to cover the descending and
ascending tracks near the periareion as well as at locations away from the
periareion, mainly to serve as background information.

Figure 3 shows the MOM tracks for the 11-orbits during December 2014 with the
MENCA observation periods indicated by purple colour along these tracks. It
can be seen that the observations have been taken at different sections of the
track ensuring the altitudes near the periareion of MOM for observing the
exosphere of Mars starting from about 260 km altitude. This height coverage of
composition data is unique as there have been hardly any such in-situ
measurements of Martian upper atmosphere deep into the exosphere.

The in-orbit mass spectrometric observations of MENCA payload are linked to
the planetary co-ordinates in terms of altitude, latitude, longitude and SZA.
These are determined using the auxiliary data files provided along with the
pressure data of the atmospheric constituents.

Figure 4: Partial pressures of exospheric constituents from MENCA observations
related to MOM orbit $\\#$55 on 24 December 2014, shown for both inbound and
outbound separately along with the coverage of solar zenith angle, latitude
and longitude.

The MENCA dataset obtained following the above procedure has been analysed
mainly to study the variation of partial pressures of gaseous composition. The
exospheric profiles of these parameters are obtained for individual orbits
specifically covering the region around periareion of MOM. A typical example
of the altitude profiles of exospheric constituents derived from MENCA
observations of the orbit $\\#$54 for descending and ascending tracks of MOM
is shown in Figure 4. Here we notice that pressure values drop with altitude
by about 2-orders of magnitude between 260 and 500 km for descending track.
However for the ascending part of orbit (a) the gradient is slower
particularly between 260-350 km and (b) the absolute magnitude of this
reduction in pressure is lower between 260 and 350 km during sunset as evident
from the variation of SZA shown in the same figure. Combined together this
decrease of pressure is slower with altitude compared to the Viking results
below 200 km due to the difference that above the thermosphere the gaseous
elements follow diffusion equilibrium paths which are different for different
species depending on their mass and temperature. It can also be seen that in
the region of exosphere above 300 km, $CO_{2}$, $N_{2}$, $O$ and $O_{2}$ are
the dominant atmospheric constituents. The figure also shows considerable
reduction in atomic oxygen ($O$) density after sunset. This is because $O$ is
mainly a product of photo-dissociation of $O_{2}$ and $CO_{2}$. As it is not
possible to separate $N_{2}$ and $CO$ having the same amu of 28, the effective
value of $N_{2}$ would be less due to the contribution of $CO$ which is a
photo-dissociation product of $CO_{2}$ (Bhardwaj et al., 2017).

Figure 5: Total pressure values estimated by summing partial pressures of a
number of constituents for the minimum altitude of each MOM orbit
($\\#$28-107) during the period October 2014 to May 2015.

Figure 6: Variation of smoothed daily mean sunspot numbers (SSN) and solar
zenith angle for the same period as in Figure 5

Figure 7: Vertical profiles of total pressure for the inbound and outbound of
MOM orbits during December 2014

Hence, for the rest of the paper we would consider $CO_{2}$, $O$, $N_{2}$ and
$Ar$ as the main elements to study the variation of exospheric composition.
Also the total pressure is computed by summing up the partial pressures of all
the major constituents as $CO_{2}$, $O$, $N_{2}$, $CO$, $NO$, $O_{2}$, $Ar$,
$N$ and $He$. The contribution due to water vapour ($H_{2}O$), $H_{2}$ and $H$
are excluded from the sum as these constituents may be modulated by the
degassing of the spacecraft.

The total pressure profiles for exospheric altitudes have been estimated from
the available data per orbit and the results for both descending and ascending
tracks are shown for the month of December, 2014 in Figure 7. It can be seen
that, except for the anomalous pressure values on 24 December 2014 (reasons
explained later in this paper), there is a progressive decrease in total
pressure at all altitudes from beginning to end of the month and this decrease
correlates well with the decrease in the smoothed daily mean SSN mentioned
earlier. The large reduction of the total pressure within a month is difficult
to explain without invoking the solar activity effects. By checking the
variations of solar zenith angles, latitude and longitude, it is found that
the effect of these is at best very marginal and not effective in bringing a
change of this magnitude within a period of one month for these altitudes.

Figure 8: Mean total pressure profiles with standard errors derived from all
the measurements during December 2014 both for inbound and outbound.

The changes in solar UV/X-ray fluxes arriving at Mars due to variations of
solar activity have a strong modulating effect on exospheric pressures. These
variations due to solar activity are found to be larger than the standard
errors at 95% confidence interval for both descending and ascending orbital
paths as shown in Figure 8. Where the standard error of the means at 95%
confidence interval is given by m$\pm$(1.96 $\sigma/\sqrt{n}$) (m is the
sample mean, $\sigma$ the standard deviation of mean and $n$ is the number of
samples). These error bars are shown in the figure.

Only at two points between 250-300 km altitude for the descending track of the
orbit the error bars are relatively large due to the anomalous pressures
profile of 24 December, 2014. This point is addressed again later in this
paper. Similar analysis is carried out separately for the partial pressures of
$CO_{2}$ and $O$ by taking the mean pressure and altitude values of descending
and ascending part of the orbital tracks. Figure 9 shows the individual days
profiles of $CO_{2}$ and $O$ pressures for different days (pertaining to
different orbit numbers) of December, 2014. From this figure the following
points can be noted: (a) there is a considerable day to day variability of the
altitude profiles of both $CO_{2}$ and $O$ with a consistent decrease of
pressure at all atitudes with respect to advancing days in the period, (b) the
$O$ pressure is generally higher than that of $CO_{2}$ before the anomalous
event of 24 December, (c) On 24 December there is a large enhancement of
$CO_{2}$ and $O$ pressures between 260-350 km when the solar proton fluxes are
enhanced due to a moderate Coronal Mass Ejection (CME) event in progress
during 20-25 December, 2014, (d) the anomalous pressure values of $CO_{2}$ are
higher than that of $O$ at all altitudes and the $O$ pressures are lower
compared to $CO_{2}$ pressures while recovering from the event after 24
December 2014.

In order to explore the anomalous pressure enhancements of $CO_{2}$ and $O$,
contours of these pressures with respect to time and altitude are plotted for
periods between 21-27 December and 18-27 December respectively. These figures
depict the following (a) $CO_{2}$ pressures started increasing anomalously
from 22 December and reached 2 peaks by 24 December one around 275 km and the
other around 295 km, (b) atomic oxygen ($O$) started increasing from 19
December and reached one strong peak around 275 km between 22-23 December, (c)
the height range of the effect of pressure increase for $CO_{2}$ continued
above 360 km but for $O$ it tapered around 330 km.

Figure 9: MOM orbit-wise vertical profiles of partial pressure measured during
December 2014

Figure 10: Partial pressure contours of $CO_{2}$ during 21-27 December 2014
(left) and partial pressure contours of $O$ during 18-27 December 2014 from
MENCA observations (right).

As can be inferred from the results presented in Figure 5 and 6, these
anomalous effects are to be examined in terms of any eruptive events of solar
high energy electromagnetic and charged particle radiations during December
2014 as both these radiations would arrive at Mars and interact with its
atmosphere and surface. Since Mars does not have a magnetic field like that of
Earth, in addition to the UV/X-rays, the charged particles or solar plasma
components like energetic protons would directly interact with the neutral
composition and initiate photodissociation and photoionisation. The absorption
of particle energy would also result in modified densities of atmospheric
species in certain height ranges due to increase in temperatures. To check
this phenomenon, the solar activity as revealed by the smoothed daily mean SSN
mentioned earlier, and the solar proton densities measured by Advanced
Composition Explorer (ACE) spacecraft (srl.caltech.edu, 2018) are plotted for
December 2014 and shown in Figure 11.

Figure 11: Variation of smoothed daily mean sunspot numbers and solar proton
flux along with moving averages during December 2014

The UV/X-rays fluxes for the period did not show any occurrence of solar flare
during this period and hence are not shown in the figure. While it is known
that CMEs, in large number of cases, are preceded by solar flares, it is also
shown that this may not always be the case and occurrence of moderately strong
CMEs during a very low solar activity year of 2009 has occurred without any
associated solar flare (Nagaraja et al., 2018). Hence the variation of solar
proton fluxes along with their moving averages in Figure 11 indicates that
there is a large increase of energetic proton fluxes during the period 21-26
December 2014 with peak fluxes around 23 December 2014. The exact date of the
peak of the corresponding $CO_{2}$ and $O$ pressures is not accurately
discernable due to the fact that the observation periodicity is restricted to
about 3-days orbital period of MOM. However it is clear the peaks of enhanced
pressures of $CO_{2}$ has occurred with a delay of about 1 to 1.5 days. The
$O$ pressures on the other hand show a broad maximum. The density of $O$
depends, apart ftom temperature on the photolysis and photo-ionisation effects
and hence its enhancement and persistence of its peak values is primarily a
cumulative effect compared to that of $CO_{2}$.

## Summary and Conclusion

  * •

MENCA data available from ISSDC in its nearly raw form has been processed to
create a new data set with orbit-wise data assimilation particularly between
250 and 500 km for the period from September 2014 to September 2015. The
ancillary information on the altitude, latitude, longitude and solar zenith
angles obtained using the SPICE kernels have been tagged to each epoch of time
and measurement. Also the partial pressures of the exospheric constituents of
Mars have been converted to actual values using the calibration information
and normalisation procedure.

  * •

As a sample of altitude-pressure profiles of exospheric constituents $CO_{2}$,
$O$, ($N_{2}$+$CO$), $O_{2}$,$NO$, $He$ & $Ar$ obtained from MOM orbit $\\#$55
near the periareion (covering $\approx$ 262-500 km in the exosphere of Mars)
are generated and presented using this data set. The variation of profiles
generally follows the exponential decrease with altitude with atomic Oxygen
($O$) concentrations being larger than that of $CO_{2}$ during daylight hours.

  * •

The total pressure is computed by summing up the partial pressures of all the
major constituents i.e., $CO_{2}$, $O$, $N_{2}$, $CO$, $NO$, $O_{2}$, $Ar$,
$N$ and $He$ to ensure removing those constituents affected by the degassing
problem. The variation of total pressure at the periareion altitudes during
October 2014 to May 2015 is well correlated with the smoothed daily sunspot
numbers.

  * •

A time series plot of partial pressure-altitude profiles of $CO_{2}$ and $O$
during December 2014 shows gradual decrease in partial pressure values (at all
altitudes) from beginning to end of December which is again consistent with
the decrease in the smoothed daily sunspot numbers.

  * •

Superimposed on the December 2014 profiles of partial pressures of $CO_{2}$
and $O$ , there are anomalously high pressure profiles of both $CO_{2}$ and
$O$ peaking on 24 December 2014. The contour plots show more details of the
temporal and altitude build up, maximum and recovery phases of this anomaly.
It is found that this anomaly is due to a moderately strong CME event of sun
which peaked around 23 December 2014.

## Acknowledgement

The authors are grateful to Indian Space Research Organisation (ISRO) for
providing necessary funds to carry out this work under a research project vide
reference ISRO:SPL:01.01.33/16. We acknowledge the use of data from the Mars
Orbiter Mission (MOM), first inter-planetary mission of the Indian Space
Research Organisation (ISRO), archived at the Indian Space Science Data Centre
(ISSDC) and, NASA’s Navigation and Ancillary Information Facility (NAIF) for
the necessary SPICE kernels of Mars. Thanks are also due to Dr. Anil Bhardwaj,
MENCA Principal Investigator and the payload team members notably, Dr. Smitha
V. Thampi, Dr. T. P. Das, Dr. M. B. Dhanya, Space Physics Laboratory (SPL),
Vikram Sarabhai Space Center for their valuable inputs and discussions.

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