# Towards Improving Faithfulness in Abstractive Summarization

Xiuying Chen1 Mingzhe Li2 Xin Gao1∗ Xiangliang Zhang3,1

1 Computational Bioscience Reseach Center, King Abdullah University of Science
and Technology
2 Ant Group
3 University of Notre Dame
{xiuying.chen<EMAIL_ADDRESS>limingzhe.lmz<EMAIL_ADDRESS>corresponding author

###### Abstract

Despite the success achieved in neural abstractive summarization based on pre-
trained language models, one unresolved issue is that the generated summaries
are not always faithful to the input document. There are two possible causes
of the unfaithfulness problem: (1) the summarization model fails to understand
or capture the gist of the input text, and (2) the model over-relies on the
language model to generate fluent but inadequate words. In this work, we
propose a Faithfulness Enhanced Summarization model (FES), which is designed
for addressing these two problems and improving faithfulness in abstractive
summarization. For the first problem, we propose to use question-answering
(QA) to examine whether the encoder fully grasps the input document and can
answer the questions on the key information in the input. The QA attention on
the proper input words can also be used to stipulate how the decoder should
attend to the source. For the second problem, we introduce a max-margin loss
defined on the difference between the language and the summarization model,
aiming to prevent the overconfidence of the language model. Extensive
experiments on two benchmark summarization datasets, CNN/DM and XSum,
demonstrate that our model significantly outperforms strong baselines. The
evaluation of factual consistency also shows that our model generates more
faithful summaries than baselines111https://github.com/iriscxy/FES.

##  1 Introduction

In recent years, text generation has made impressive progress [1, 2, 3]. The
abstractive summarization task, aiming to produce a concise and fluent summary
that is salient and faithful to the source document, has become a research
hotspot due to its broad application prospect. The prevalence of pretrained
transformer language models (LM) [4, 5] has largely improved the fluency and
salience of generated summaries. However, studies [6, 7] showed that many
summarization models suffer from unfaithfulness problem, _i.e.,_ the generated
summary is not entailed by the information presented in the source document.
Durmus et al. [8] highlighted two notions of the unfaithfulness problem in
summarization: one is the manipulation of information presented in the input
document (intrinsic errors), and the other is the inclusion of information not
inferable from the input (extrinsic errors).

The Intrinsic error problem is often caused by the failure of document level
inference, which is necessary for abstractive summarization. Specifically, the
summarization model has misinformation inferred from the input document
because of an inadequate encoder that misunderstands the source semantic
information and a poor decoder that cannot fetch relevant and consistent
content from the encoder. Several recent summarization models were proposed
from this perspective. For example, Wu et al. [9] proposed a unified semantic
graph encoder to learn better semantic meanings and a graph-aware decoder to
utilize the encoded information. Cao et al. [10] used contrastive learning to
help the model be aware of the factual information. The second type of error,
extrinsic error, is often introduced by excessive attention paid to the LM,
which ensures fluency while neglecting to summarize the source document. For
example, a LM is inclined to generate the commonly-used phrase “score the
winner” while the correct phrase is “score the second highest” which is less
frequently used. This type of error has been studied in the neural machine
translation task [11], but has not been addressed in abstractive
summarization.

To address these errors, we propose a novel Faithfulness Enhanced
Summarization model (FES). To prevent the intrinsic error problem, we design
FES in a multi-task learning paradigm, _i.e.,_ completing encoding-decoding
for the summarization task with an auxiliary QA-based faithfulness evaluation
task. The QA task poses an additional reasoning requirement on the encoder to
have a more comprehensive understanding on the key semantic meanings of the
input document and learn better representations than working only for
summarization. The QA attention on the key entities of the input can also be
used to align the decoder state with the encoder outputs for generating a
faithful summary. To address the extrinsic error problem, we propose a max-
margin loss to prevent the LM from being overconfident. Concretely, we define
an indicator of the overconfidence degree of the LM. The risk of outputting
extrinsic error tokens with low prediction probabilities is mitigated by
minimizing this overconfidence indicator.

We validate the effectiveness of our FES model by conducting extensive
experiments on public benchmark CNN/DM [12] and XSum [13] datasets.
Experimental results demonstrate that our faithfulness enhanced summarization
model has superior performance on the ROUGE scores and improves the
faithfulness of news summarization over several strong baselines.

Our main contributions can be summarized as follows. (1) We propose a
faithfulness enhanced summarization model, which alleviates the unfaithfulness
problem from the encoder side and decoder side. (2) Concretely, we propose a
multi-task framework to enhance the summarization performance by automatic QA
tasks. We also propose a max-margin loss to control the overconfident problem
of the LM. (3) Experimental results demonstrate that our proposed approach
brings substantial improvements over the most recent baselines on benchmark
datasets, and can also improve the faithfulness of the generated summary.

##  2 Related Work

Abstractive Summarization. In recent years, the research on text generation
has made impressive progress [14, 15, 16], which promotes the progress of
abstractive summarization. The abstractive summarization task generates novel
words and phrases not featured in the source text to capture the salient ideas
of the source text [17]. Most works apply an encoder-decoder architecture to
implicitly learn the summarization procedure [18, 19]. More recently, applying
pretrained language models as encoder [4, 20] or pre-training the generation
process by leveraging a large-scale of unlabeled corpus [21, 22] brings
significant improvements. Explicit structure modeling has also been shown to
be effective in summarization tasks. For example, Jin et al. [23] incorporated
semantic dependency graphs to help generate sentences with better semantic
relevance, and Wu et al. [9] came up with a unified semantic graph to
aggregate relevant disjoint context from the input.

Fact Consistency for Abstractive Summarization. Producing a summary that is
entailed by the information presented in the source document is a key
challenge in the summarization task, and less progress has been made on it.
Pioneer works [24, 25] incorporated fact descriptions or entailment knowledge
to enhance faithfulness. More recently, Zhu et al. [26] modeled the facts in
the source article with knowledge graphs based on a graph neural network. Cao
et al. [10] proposed to leverage reference summaries as positive training data
and erroneous summaries as negative data, to train summarization systems that
are better at distinguishing between them. Aralikatte et al. [27] introduced
focus attention mechanism to encourage decoders to proactively generate tokens
that are similar or topical to the input document. On the contrary, other
works post-edit the generated summaries. Different from previous works, we
enhance the semantic understanding of the document with faithfulness
evaluation as a direct signal and prevent the overconfidence of LM which is
not addressed before.

Multi-task Learning. Multi-task learning is a learning paradigm in machine
learning and it aims to leverage useful information contained in multiple
related tasks to help improve the generalization performance of all the tasks
[28]. There is a large quantity of natural language processing tasks
formulated by multi-task learning, such as word segmentation, POS tagging,
dependency parsing, and text classification [29, 30, 31, 32]. In this work, we
apply multi-task learning to summarization and question-answering tasks for
faithfulness enhancement.

Figure 1:  The comparison of the existing QA-based faithfulness evaluation
model and our faithfulness-enhanced summarization model. The QA task
integrated in our model provides an auxiliary supervision signal to understand
the document in the training process and enhance the faithfulness of the
generated summary.

##  3 Methodology

###  3.1 Problem Formulation

For an input document $X=\\{x_{1},...,x_{n_{x}}\\}$, we assume there is a
ground truth summary $Y=\\{y_{1},\dots,y_{n_{y}}\\}$. In our faithfulness
enhanced setting, $n_{q}$ question answering pairs
$Q=\\{Q^{1},...,Q^{n_{q}}\\}$ with corresponding answers
$A=\\{A^{1},...,A^{n_{q}}\\}$ are also attached with $X$. In the training
process, our model is given QA pairs and document-summary pairs. It tries to
extract answers $A$ to the questions and generate the summary $Y$. In test
stage, our model is given document $X$ and questions $Q$, and predicts the
answers and summary. The final goal is to generate a summary that is not only
informative but also consistent with document $X$.

Following, we introduce our proposed _Faithfulness Enhanced Summarization_
model, which is generally built on Transformer [33]. The faithfulness
enhancement is implemented from three aspects: (1) Multi-task Encoder. It
improves the semantic understanding of the input document by examining the
quality of the encoded document representations for an auxiliary QA task. The
encoded representation thus captures the key inputs for making faithful
summary. (2) QA Attention-enhanced Decoder. The attention from the multi-task
encoder aligns the decoder with the encoder so that the decoder can fetch more
accurate input information to generate the summary. (3) Max-margin Loss. This
is a loss orthogonal to the generation loss. It measures the accuracy of the
LM and prevents it from being overconfident in the generation process.

Figure 2: Multi-task encoder.

###  3.2 Multi-task Encoder

The multi-task encoder is designed for encoding the input document for both
summarization and question-answering in an integrated training process, as
shown in Figure 1(b). This is different from the previous work that uses QA in
the post-generation stage for evaluating the faithfulness of the generated
summaries [8, 7], as shown in Figure 1(a). We bring the QA closer to the
encoder instead of leaving it for post-generated summary, and make the encoder
be trained to accomplish the QA and summarization task in the meantime. This
integrated training of a multi-task encoder includes faithfulness also as an
optimization objective, besides the summary generation quality. The answers
are key entities from the document so that QA pairs focus on key information
in the input.

As shown in Figure 2, we first apply the classic Transformer architecture to
obtain token-level representations for the document and questions, denoted as
$\mathbf{H}_{w}\in\mathbb{R}^{n_{w}\times d_{e}}$ and
$\mathbf{H}_{u}\in\mathbb{R}^{n_{q}\times t_{q}\times d_{e}}$, where $n_{w}$
is the total number of tokens in the document, $n_{q}$ is the question number,
$t_{q}$ is the token number in a question, and $d_{e}$ is the feature
dimension. Then, we design the encoder to understand the question and the
input document question from entity levels and sentence levels.

#### Encoding at Multi-level Granularity.

We build the encoder by organizing the representation learning at different
granularity levels. We use entities as the basic semantic unit as they contain
compact and salient information across the document, and the reading
comprehension questions focus on entities. Since a question is usually short,
we create one node for each question. We add bidirectional edges from the
questions to sentence nodes, and from sentence to entity nodes. These nodes
act as the intermediary between sentences and enrich the cross-sentence
relations. Because the initial directed edges are insufficient for learning
backward information, we add reverse edges and self-loop edges to the graph
following previous works [34]. We initialize node representations following
the token level and word span level mean-pooling process [9].

Given the constructed graph with node features, we use graph attention
networks [35] to update the representations of our semantic nodes. We refer to
$\tilde{h}_{i}\in\mathbb{R}^{d_{e}},i\in\\{1,\cdots,(n_{e}+n_{s}+n_{q})\\}$ as
the hidden states of input nodes, where $n_{e}$ and $n_{s}$ are the number of
entity nodes and sentence nodes, respectively. The graph attention (GAT) layer
is designed as follows:

$\displaystyle
z_{ij}=\operatorname{LeakyReLU}\left(W_{a}\left[W_{b}\tilde{h}_{i};W_{c}\tilde{h}_{j}\right]\right),\quad\alpha_{ij}=\frac{\exp\left(z_{ij}\right)}{\sum_{l\in\mathcal{N}_{i}}\exp\left(z_{il}\right)},\quad
l_{i}=\sigma(\textstyle\sum_{j\in\mathcal{N}_{i}}\alpha_{ij}W_{d}\tilde{h}_{j}),$

where $\mathcal{N}_{i}$ is the set of neighboring nodes of node $i$, $W_{a}$,
$W_{b}$, $W_{c}$, $W_{d}$ are trainable weights and $\alpha_{ij}$ is the
attention weight between $\tilde{h}_{i}$ and $\tilde{h}_{j}$. Besides, we add
a residual connection to avoid gradient vanishing after several iterations:
$h_{i}=\tilde{h}_{i}+l_{i}$. We iteratively use the above GAT layer and
position-wise feed-forward layer [33] to update each node representation. The
output entity feature matrix, sentence feature matrix, and question matrix,
are denoted as $\mathbf{H}_{e}\in\mathbb{R}^{n_{e}\times d_{e}}$,
$\mathbf{H}_{s}\in\mathbb{R}^{n_{s}\times d_{e}}$, and
$\mathbf{H}_{q}\in\mathbb{R}^{n_{q}\times d_{e}}$, respectively.

Answer Selector for the QA task. After fusing information from the question
and the document, we can select entities from the document as the answer to
the question. Concretely, we apply the multi-head cross attention (MHAtt)
between the question and the entities from the graph:
$h^{i}_{qe}=\operatorname{MHAtt}\left(h^{i}_{e},\mathbf{H}_{q},\mathbf{H}_{q}\right)$
to obtain question-aware entity representations, where $i$ is the question
index. Based on the question-aware entity representations, we employ a feed-
forward network (FFN) to generate the entity extracting probabilities
$A^{i}=\text{FFN}(h^{i}_{qe})$, where $A^{i}=(a^{i}_{1},...,a^{i}_{n_{e}})$.
The QA objective is to maximize the likelihood of all ground-truth entity
labels $\hat{a}$:

$\mathcal{L}_{c}=\textstyle\sum^{n_{q}}_{i=1}\sum_{j=1}^{n_{e}}P\left(\hat{a}^{i}_{j}\right).$
(1)

###  3.3 QA Attention-enhanced Decoder

A faithful decoder needs to attend to and fetch the important content from the
encoder instead of mixing the inputs. We observe from §3.2 that the QA
attentions on the key entities can be regarded as importance signals
indicating which entities should be included in the summary. Hence, we propose
a summary generator enhanced by QA attention. Generally, the decoder state
attends to the encoder states with entities as intermediates, where the
entity-level attention is guided by QA attentions.

Concretely, for each layer, at the $t$-th decoding step, we apply the self-
attention on the masked summary embeddings $\mathbf{E}$, obtaining $u_{t}$.
The masking mechanism ensures that the prediction of the position $t$ depends
only on the outputs before $t$. Based on $u_{t}$, we then compute the cross-
attention scores $c_{t}^{e}$ over entities.

$\displaystyle
u_{t}=\operatorname{MHAtt}\left(e_{t},\mathbf{E}_{<t},\mathbf{E}_{<t}\right),c_{t}^{e}=\operatorname{MHAtt}\left(u_{t},\mathbf{H}_{e},\mathbf{H}_{e}\right).$
(2)

In effect, the first attention layer captures contextual features of the
decoded sequence, while the second incorporates entity information in
$c_{t}^{e}$. Herein, we minimize the bidirectional Kullback-Leibler (KL)
divergence between the QA attention $A^{i}$ and the summarization attention
$E_{t}$ on the entities at the $t$-th step, to help the summarization model
learn what entities are important:

$\displaystyle\mathcal{L}_{KL}=\mathcal{D}_{KL}\left(\textstyle\sum_{i=1}^{n_{q}}A^{i}\|\textstyle\sum_{t=1}^{n_{y}}E_{t}\right),$
(3)

where $n_{y}$ is the number of tokens in the ground truth summary. Note that
this objective guides the cross-attention to capture correct contextual
information rather than to only learn the QA attention distribution. So we
only employ it on parts of attention heads to avoid “overfitting” to the QA
task. We then use the entity-level attention to guide the selection of source
tokens related to the key entities, by applying another MHAtt layer on source
word sequence $\mathbf{H}_{w}$ and $c_{t}^{e}$:

$\displaystyle
v_{t}=\operatorname{MHAtt}\left(c_{t}^{e},\mathbf{H}_{w},\mathbf{H}_{w}\right).$
(4)

This context vector $v_{t}$, treated as salient contents summarized from
various sources, is sent to an FNN to produce the distribution over the target
vocabulary, _i.e.,_ $P_{t}=\text{Softmax}\left(\text{FFN}(v_{t})\right)$. All
the learnable parameters are updated by optimizing the negative log likelihood
objective function of predicting the target words:

$\displaystyle\mathcal{L}_{s}=-\textstyle\sum_{t=1}^{n_{y}}\log
P_{t}\left(y_{t}\right).$ (5)

###  3.4 Max-margin Loss

Figure 3:  Max-margin loss for summarization model. (a) When the LM is
accurate (the highest $P_{word}$ in blue for the target word), the correct
target word is also predicted by both baseline and our summarization model
with the highest $P_{word}$. (b) When the LM is not accurate enough (the
highest $P_{word}$ in blue for a wrong candidate word2), our model can prevent
the overconfidence of LM by max-margin loss and predict the correct target
word, while the baseline model does not.

Previous works [36, 37] suggest that a poorly-informed decoder will neglect
some source segments, function more as an open-ended LM, and thus will be
prone to extrinsic errors. Inspired by faithfulness enhanced machine
translation works [38, 39], we introduce a max-margin loss into the
summarization task, for maximizing the difference of the predicted probability
for each token of the summarization model and LM as shown in Figure 3, which
suppresses the tendency of summarizer to generate common but unfaithful words.
Concretely, we first define the margin between the summarizer and the LM as
the difference of the predicted probabilities:

$\displaystyle m_{t}=P_{t}\left(y_{t}\mid
y_{<t},X\right)-P_{t}^{LM}\left(y_{t}\mid y_{<t}\right),$ (6)

where $X$ is the input document, and $P^{LM}_{t}$ denotes the predicted
probability of the $t$-th token of the LM. Note that the language model has no
access to the input document, and only takes the decoded summary prefix as
input. Intuitively, if $m_{t}$ is large, then the summarization model is
apparently better than the LM. When $m_{t}$ is small, there are two
possibilities. One is that both the LM and the summarization model have good
performance, hence the predicted probabilities should be similar. The other
possibility is the LM is not good enough but overconfident, which leads to a
summarizer with poor performance.

Hence, we present the max-margin loss $\mathcal{L}_{m}$, which adds a
coefficient to the margin:

$\displaystyle\mathcal{L}_{m}=\textstyle\sum_{t=1}^{n_{y}}\left(1-P_{t}\right)\left(1-m_{t}^{5}\right)/2,$
(7)

where we abbreviate $P_{t}(y_{t}\mid y_{<t},X)$ as $P_{t}$. The term
$(1-m_{t}^{5})/2$ is a non-linear monotonically decreasing function in regard
to $m_{t}$, which ensures the optimization of maximizing $m_{t}$. We choose
Quintic function (fifth power) here as it is shown to be more stable [39]. The
first factor $\left(1-P_{t}\right)$ is for fitting the two possibilities we
discussed above. When $P_{t}$ is large, the summarization model learns the
$y_{t}$ well and does not need to pay too much attention on $m_{t}$. This is
reflected by $(1-P_{t})$, a small coefficient of $m_{t}$. On the other hand,
when $P_{t}$ is small, it means that the summarizer needs to be better
optimized, and a large coefficient $(1-P_{t})$ enables the model is able to
learn from the margin information.

The above four losses, $\mathcal{L}_{c}$, $\mathcal{L}_{s}$,
$\mathcal{L}_{KL}$, and $\mathcal{L}_{m}$ are orthogonal and can be combined
to improve faithfulness.

##  4 Experiment

###  4.1 Experimental Setup

Datasets. We demonstrate the effectiveness of our approach on two public
datasets, CNN/DM and XSum, which have been widely used in previous
summarization works. Both datasets are based on news and consist of a large
number of events, entities, and relationships that can be used to test the
factual consistency of summarization models.

Note that our summarization model is accompanied by a QA task. Hence, we pre-
construct QA pairs for each case using QuestEval tool provided by Scialom et
al. [7]. Concretely, QuestEval first selects a set of the named entities and
nouns as answers from the source document. Then, it uses a finetuned answer-
conditional question generation T5 [40] model to generate questions via beam
search. To ensure the quality of the QA pairs, we only select those questions
for which the question-answering model [7] gives the right answers. Finally,
we take 38 QA pairs for CNN/DM and 27 pairs for XSum on average. For training
the summarization model, intuitively, we want the questions to focus on the
key information in the input. Hence, we select the pairs where the answers
have the highest ROUGE-L scores with the target summaries as oracle pairs. A
BART-based extraction model [22] is then trained to predict important answers
(QA pairs). Dou et al. [41] showed that it brings more benefits when using the
oracle guidance in the training phase. Hence, for the training dataset, we use
the oracle QA pairs, _i.e.,_ the first 8 pairs with the highest ROUGE scores
as input. For validation and test, we use the pairs selected by the extraction
model.

Baselines. We first compare our model with recent factual-consistent
summarization models: (1) FASum [26] is a model that extracts and integrates
factual relations into the summary generation process via graph attention. (2)
CLIFF [10] leverages reference summaries as positive data and erroneous
summaries as negative data to train summarization systems. We also compare our
proposed model with recent abstractive summarization models: (3) BART [22] is
a state-of-the-art abstractive summarization model pretrained with a denoising
autoencoding objective. (4) PEGASUS [21] is a pre-training large Transformer-
based encoder-decoder models for summarization task. (4) GSum [41] is a
summarization framework that can take external sentence guidance as input. (5)
SimCLS [42] bridges the gap between the learning objective and evaluation
metrics by a reference-free evaluation.

Implementation Details. We implement our experiments in Huggingface [43] on 4
NVIDIA A100 GPUs. We build our models based on BART (facebook/bart-large) for
CNN/DM and PEGASUS (google/pegasus-xsum) for XSum following their
hyperparameter settings, as they obtain better performance on each dataset,
respectively. The QA number is set to 8 unless otherwise stated. To avoid the
model from learning the position information of entities or questions, we sort
the entities and questions in alphabetical order. We use Adam optimizer with
$\epsilon$ as 1e-8 and $\beta$ as (0.9, 0.999). The learning rate is set to
3e-5. The warm-up is set to 500 steps for CNN/DM and 125 for XSum. The batch
size is set to 8 with gradient accumulation steps of 4. The beam size is set
to 6 for CNN/DM and 8 for XSum. For pretrained LM models, we finetune the
vanilla BART-based or PEGASUS-based LM on the CNN/DM and XSum dataset,
respectively.

###  4.2 Main Results

Automatic Evaluation. We evaluate models using standard full-length ROUGE F1
[44]. ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L) refer to the matches
of unigram, bigrams, and the longest common subsequence, respectively. We then
use BERTScore [45] to calculate a similarity score between the summaries based
on their BERT embeddings. We also evaluate our approach with the latest
factual consistency metrics, FactCC [6] and QuestEval [7]. FactCC is a weakly-
supervised, model-based approach for verifying factual consistency. QuestEval
considers not only factual information in the generated summary, but also the
most important information from its source text, and finally gives a weighted
F1 score QE.

The results are shown in Table 1. Among factual-consistent baselines, FASum
performs relatively poorly. One possible reason is that FASum is not trained
on pretrained models. CLIFF achieves better BERTScore and faithfulness scores
than strong baseline BART. It can be seen that our model outperforms GSum by
0.97 ROUGE-1, 0.90 BERTScore on CNN/DM dataset and 3.35 QE score on XSum
dataset, indicating questions in the multi-task provides better signals than
important sentences. Finally, our model outperforms the best baseline SimCLS
significantly in most of the metrics, especially in terms of faithfulness
metrics (FactCC and QE), which proves the effectiveness of our methods. We
also show the performance of our model on the test dataset when using the
oracle QA pairs to evaluate the upper bound of the benefits brought by the QA
task. We can see that oracles improve the performance significantly, with the
best-performing model achieving a ROUGE-1 score of 50.50. The results indicate
that 1) the model performance has the potential to be further improved given
better QA pairs; and 2) the model does benefit from the auxiliary QA task.

Human Evaluation. Since automatic evaluations are not perfect and can be
misleading sometimes, we further conduct a pairwise human evaluation to see
whether our generated summaries are faithful to the source document. Following
Cao et al. [10], we randomly sample 100 cases from CNN/DM and XSum, and then
hire two fluent English speakers to evaluate summary informativeness (Inform.)
and factual consistency (Factual.). For each article, the annotators are shown
summaries generated by the baseline BART or PEGASUS model and two other
systems. They then rate each system summary against the baseline summary.
Next, the annotators are asked to label text spans with intrinsic and
extrinsic errors. The compared models are baselines that achieve high
automatic scores, and are shown without system names.

Table 1: Comparisons with state-of-the-art models on CNN/DM and Xsum. Marked ROUGE results are from [41, 42]. Numbers in bold mean that the improvement to the best baseline is statistically significant (a two-tailed paired t-test with p-value <0.01).  Dataset | Model | Traditional Metric | Advanced Metric
---|---|---|---
RG-1 | RG-2 | RG-L | BERTScore | FactCC | QE
CNN/DM | FASum | 42.75 | 20.07 | 39.83 | 88.35 | 49.86 | 30.30
CLIFF | 44.16 | 21.13 | 41.06 | 88.62 | 51.58 | 33.28
BART* | 44.66 | 21.53 | 41.35 | 88.36 | 51.11 | 31.93
PEGASUS* | 44.17 | 21.47 | 41.11 | 88.27 | 50.98 | 31.84
GSum* | 45.94 | 22.32 | 42.48 | 88.64 | 53.51 | 33.98
SimCLS* | 46.67 | 22.15 | 43.54 | 88.78 | 53.21 | 33.83
FES | 46.91 | 22.84 | 43.47 | 89.54 | 55.23 | 35.50
| FES(oracle) | 50.50 | 26.41 | 46.97 | 90.01 | 59.25 | 39.11
| FES w/o multi | 44.95 | 21.86 | 41.61 | 88.93 | 54.18 | 34.90
| FES w/o QA attention | 46.53 | 22.49 | 43.12 | 89.27 | 54.85 | 35.22
| FES w/o margin | 46.23 | 22.26 | 42.82 | 89.22 | 54.60 | 34.93
| FES w/o key | 45.50 | 22.25 | 42.12 | 89.03 | 54.47 | 34.78
XSum | FASum | 42.18 | 19.53 | 34.15 | 90.46 | 17.27 | 19.01
CLIFF | 44.47 | 21.39 | 36.41 | 91.48 | 20.05 | 22.91
BART* | 45.51 | 21.94 | 36.75 | 91.32 | 19.91 | 20.36
PEGASUS* | 47.21 | 24.56 | 39.25 | 91.25 | 20.69 | 22.63
GSum* | 45.40 | 21.89 | 36.67 | 90.45 | 20.61 | 22.48
| SimCLS* | 47.61 | 24.57 | 39.44 | 91.28 | 20.80 | 22.97
| FES | 47.77 | 24.95 | 39.66 | 92.05 | 22.34 | 25.83

Table 2: Human evaluation: percentages of summaries that are worse than, tied with, or better than BART on CNN/DM dataset.  | Inform. | Factual.
---|---|---
Model |  Lose$\downarrow$ |  Tie |  Win$\uparrow$ |  Lose$\downarrow$ |  Tie |  Win$\uparrow$
SimCLS |  9% |  75% |  16% |  7% |  85% |  8%
FES |  6% |  72% |  22% |  7% |  81% |  12%
Table 3: Human evaluation: percentages of summaries that are worse than, tied with, or better than PEGASUS on XSum dataset. | Inform. | Factual.
---|---|---
Model |  Lose$\downarrow$ |  Tie |  Win$\uparrow$ |  Lose$\downarrow$ |  Tie |  Win$\uparrow$
SimCLS |  7% |  86% |  7% |  8% |  79% |  13%
FES |  6% |  83% |  11% |  6% |  78% |  16%

Table 4.2 and Table 4.2 show the manual evaluation results. Firstly, we can
find that there are larger differences in terms of Informativeness on the
CNN/DM datasets and in Factual consistency on XSum datasets. This echos the
attributes of the datasets, where the summaries in CNN/DM are longer and cover
more detailed information, while summaries in XSum are shorter and thus
require the summarization model to have advanced summarization ability.
Secondly, our model is more frequently rated as being more informative and
more factual than SimCLS summaries on both datasets. This is consistent with
our automatic evaluation metrics. The kappa statistics are 0.53 and 0.59 for
informativeness and factual consistency respectively, indicating the moderate
agreement between annotators. The statistical significance between FES and
PEGASUS is tested using a two-tailed paired t-test for significance for
$\alpha=0.05$.

Figure 4:  (a) ROUGE scores w.r.t. the number of used QA pairs on CNN/DM. (b)
The distribution of margin $m_{t}$ of BART and FES. FES has $m_{t}$ much less
negative, more around 0 and closer to 1, indicating the alleviation of
overconfidence issue of LM. (c) The distributions of margin $m_{t}$ on entity
words and all words. The proportion of entity words with $m_{t}$ around 0 is
smaller, indicating that LM is accurate in predicting function words.

###  4.3 Discussions

Ablation Study. We perform an ablation study on CNN/DM dataset to investigate
the influence of different modules in FES. First, we remove the multi-task
framework to verify the effectiveness of joint learning on summarization. This
means that the QA attention-enhanced mechanism in the decoder is also removed,
so the model degrades to the BART summarizer with entity inputs and max-margin
loss. Secondly, we keep the QA task but replace the QA attention-enhanced
decoder with the vanilla Transformer decoder. Thirdly, we remove the max-
margin loss to verify the influence of the overconfidence of the LM.
Additionally, we randomly select QA pairs to see if the understanding of the
model on the document has to be related to key information.

From the results in Table 1, we find without the multi-task framework, the
performance of FES drops greatly by 1.86 RG-L score, 0.43 BERTScore, and 0.60
QE score on CNN/DM dataset, which indicates with the QA multi-task does
strengthen the encoder to learn more comprehensive representations. Next, the
QE score drops by 0.28 after the QA attention guidance is removed. This
indicates that aligning the QA attention with the summarization attention on
the important entities can help the model capture gist information from the
input, and restrict such loss on a limited part of entities can guide the
decoder to fetch meaningful content from the input. FactCC score drops by 0.63
after the max-margin loss is removed. It indicates that preventing the LM from
being overconfident can help increase faithfulness. Finally, the performance
of FES drops when using random QA pairs as guidance but outperforms BART by a
large margin. This shows that enhancing the understanding of the document is
helpful even when it is not always related to the key information. But the
performance can be further improved by asking questions on key entities.

Table 4: Automatic evaluation results of the QA task. Model | EM | F1
---|---|---
FES w/o multi | 76.34 | 83.59
FES | 77.77 | 85.29
Table 5: The percent of $m_{t}<0$ and average $m_{t}$ of our model and baseline.  Model | Percent of $m_{t}<0~{}(\downarrow)$ | Average $m_{t}~{}(\uparrow)$
---|---|---
BART | 15.83% (ref) | 0.22 (ref)
FES |  13.50% (-2.33%) |  0.33 (+0.11)

The Number of QA pairs. To investigate how the number of QA pairs influences
the performance of our model, we conduct experiments on the CNN/DM dataset
with 6-14 oracle QA pairs. From results in Figure 4(a), we see that the ROUGE
score increases with the number of QA pairs, to begin with. After reaching 8
pairs, the improvements begin to vanish. One possible reason is that the
answers no longer focus on the important information in the document. Note
that the performance of FES remains at a high level in the range of 8-15 QA
pairs, demonstrating the effectiveness and robustness of FES. At last, we
choose to include 8 QA pairs in our model as default.

QA Task Evaluation. Since our framework also involves a question answering
task, investigating the QA performance is helpful for understanding the model.
Concretely, we use exact match (EM) and partial match (F1) to evaluate FES
model and its ablation model, as shown in Table 4.3. Firstly, we can see that
with the multi-task framework, both scores show significant improvements. This
demonstrates that the two tasks can benefit each other, and the summarization
task can also enhance QA performance. Secondly, the EM and F1 scores of QA are
relatively high, showing that our model can also be used for answering
questions on the document.

Table 6: Examples of summaries generated by baseline models and our method.
The original fact, intrinsic error, extrinsic error and the corresponding
faithful fact in each summary are highlighted. Italic words are those
predicted by the LM.

Relevant QA pair | Relevant Context | Baseline Summary (truncated) | Our Summary (truncated)
---|---|---|---
Q: Who is the unexpected scorer of QPR’s equaliser? A: Clint Hill |  Defender Clint Hill was the unexpected scorer of QPR’s equaliser just after half-time to make it 2-2. Hill grabs the Queens Park Rangers badge in celebration after scoring his first ever Premier League goal. |  Christian Benteke equalised for the hosts just three minutes later. Clint Green put QPR ahead just after the half hour mark . |  Defender Clint Hill scored his first ever Premier League goal to make it 2-2 just after half time at Villa Park.
Q: What did Ecuador issue after the Costa Rican government complained to the Ecuadorian authorities? A: An apology |  The Ecuadorain Ambassador Ricardo Patino then followed up with an apology to Costa Rica and confirmed Ecuador had sent a letter to the government to settle the matter. |  The stunt sparked outrage from Costa Rica, who complained to the authorities. Ecuador has since issued an apology and accepted the government’s apology. |  Costa Rican tourism minister said she was ’unhappy’ with the use of her country’s image. Ecuador has since issued an apology to Costa Rica and sent a letter of apology.
Q: Who scored one goal for Real Sociedad in the first half? A: Real Sociedad midfielder Gonzalo Castro |  Sociedad seized the lead once more in the 57th minute with a goal of the highest quality when Gonzalo Castro volleyed homeSergio Canales’cross. But Deportivo came roaring back once more |  Lucas Perez equalised for Deportivo La Coruna in the 40th minute. Gonzalo Castro scored the winner in the 57th minute for David Moyes ’ side. |  Gonzalo Castro scored one of the best goals of the season for David Moyes’ side. Verdu Nicolas headed home to give Deportivo the lead with 12 minutes remaining.
Q: What season was he loaned back to Lille? A: 2014-15 season |  The Belgium striker was signed by Liverpool in a 10million deal after impressing at the 2014 World Cup in Brazil, before being loaned back to Lille for the 2014-15 season. |  Divock Origi joined Liverpool in a £ 10million deal from Lille this summer. The Belgium striker was loaned back to Lille for the 2014 - 15 season. |  Origi was loaned back to Lille for the 2014-15 season after impressing at the World Cup.
Q: What two groups were at the height of tension? A: Asian and African americans |  On Thursday, NPR headquartered in Washington, just 40 miles away from Baltimore ran its latest update on the urban turmoil that has erupted in the wake of the death of 25-year-old Freddie Gray |  Ruben Navarrette: There ’s little evidence that Asian businesses were targeted out of racial animus. |  Ruben Navarrette: NPR report on Baltimore unrest focused on tension between African-Americans and Asians.

Margin between FES and the LM. We show the distribution of the margin $m_{t}$
defined in Eq. 6 from our FES and the BART in Figure 4(b). Firstly, there are
still many tokens with negative $m_{t}$ and a large amount of $m_{t}$ around 0
for BART. This indicates that the LM is probably overconfident for many
tokens, and addressing the overconfidence problem is meaningful for
summarization. By comparison, it can be seen that the number of negative
margin cases is significantly reduced in FES compared with BART. More
precisely, we list the percentage of tokens with negative $m_{t}$ and the
average $m_{t}$ for each model in Table 4.3. Compared with BART, FES reduces
the negative $m_{t}$ by 2.33% and increases the average of $m_{t}$ by 0.11
points. This proves that the overconfidence problem of the LM is solved to a
great extent. Besides, we draw the comparison of $m_{t}$ on all words and
entity words in Figure 4(c). It can be seen that the proportion of around 0
for entity words is significantly reduced, which verifies our assumption that
LM is accurate for many function words.

Case Study. We show several representative cases in Table 6, including the
relevant QA pairs and relevant context from the source document, summaries
generated by BART, PEGASUS, or SimCLS, and by our model on two datasets. The
first three cases show summaries with intrinsic errors from baselines, because
the baseline model misunderstands the source document. Our model makes
faithful summaries with the help of answering the questions. For the forth
case with extrinsic errors, the baseline summarization model and LM generate
similar tokens, which are not included in the source document. FES has no
extrinsic error, as it alleviates the overconfidence of the LM by introducing
the max-margin loss. It is interesting to mention that the benefits brought by
QA task and max-margin loss can complement each other. For example, in the
third case, both the QA pair and max-margin loss prevent generating unfaithful
information. We also show an error analysis in the last row. Both generated
summary include an unmentioned name, which can be found in the training
dataset. A post-edit operation might solve the problem, and we look forward to
improving it in the future.

##  5 Conclusion

In this paper, we propose the multi-task framework with max-margin loss to
generate faithful summaries. The auxiliary question-answering task can enhance
the model’s ability to understand the source document, and the max-margin loss
can prevent the overconfidence of the LM. Experimental results show that our
proposed model is effective across different datasets. In the future, we aim
to incorporate post-edit operation to improve faithfulness.

Generating faithful summaries is an important step toward real artificial
intelligence. This work has the potential positive impact on an intelligent
and engaging reading system. At the same time, if people rely too much on
summarized systems of prompt reading, they may become less capable of reading
long documents. Besides, the pre-training model may be injected with malicious
and vulgar information, and results in server misleading summary. Therefore,
we should be cautious of these advantages and disadvantages.

## Acknowledgments

We would like to thank the anonymous reviewers for their constructive
comments. This publication is based upon work supported by the King Abdullah
University of Science and Technology (KAUST) Office of Research Administration
(ORA) under Award No FCC/1/1976-44-01, FCC/1/1976-45-01, URF/1/4663-01-01, and
BAS/1/1635-01-01. This work was also supported by Alibaba Group through
Alibaba Research Intern Program.

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