# Making Science Simple: Corpora for the Lay Summarisation of Scientific
Literature

Tomas Goldsack1, Zhihao Zhang2, Chenghua Lin1111∗Corresponding author. ,
Carolina Scarton1
1Department of Computer Science, University of Sheffield, UK
2School of Economics and Management, Beihang University, China
{tgoldsack1, c.lin<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
###### Abstract

Lay summarisation aims to jointly summarise and simplify a given text, thus
making its content more comprehensible to non-experts. Automatic approaches
for lay summarisation can provide significant value in broadening access to
scientific literature, enabling a greater degree of both interdisciplinary
knowledge sharing and public understanding when it comes to research findings.
However, current corpora for this task are limited in their size and scope,
hindering the development of broadly applicable data-driven approaches. Aiming
to rectify these issues, we present two novel lay summarisation datasets, PLOS
(large-scale) and eLife (medium-scale), each of which contains biomedical
journal articles alongside expert-written lay summaries. We provide a thorough
characterisation of our lay summaries, highlighting differing levels of
readability and abstractiveness between datasets that can be leveraged to
support the needs of different applications. Finally, we benchmark our
datasets using mainstream summarisation approaches and perform a manual
evaluation with domain experts, demonstrating their utility and casting light
on the key challenges of this task. Our code and datasets are available at
https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation.

##  1 Introduction

Scientific publications contain information that is essential for the
preservation and progression of our understanding across all scientific
disciplines. Typically being highly technical in nature, such articles tend to
assume a degree of background knowledge and make use of domain-specific
language, making them difficult to comprehend for one lacking the required
expertise (i.e., a lay person). These factors often limit the impact of
research to only its direct community (Albert et al., 2015, 2022) and, more
dangerously, can cause readers (members of the public, journalists, etc.) to
misinterpret research findings (Kuehne and Olden, 2015).

Technical Abstract The virus SARS-CoV-2 can exploit biological vulnerabilities
(e.g. host proteins) in susceptible hosts that predispose to the development
of severe COVID-19. To identify host proteins that may contribute to the risk
of severe COVID-19, we undertook proteome-wide genetic colocalisation tests,
and polygenic (pan) and cis-Mendelian randomisation analyses leveraging
publicly available protein and COVID-19 datasets… Lay Summary Individuals who
become infected with the virus that causes COVID-19 can experience a wide
variety of symptoms. These can range from no symptoms or minor symptoms to
severe illness and death. Key demographic factors, such as age, gender and
race, are known to affect how susceptible an individual is to infection.
However, molecular factors, such as unique gene mutations and gene expression
levels can also have a major impact on patient responses by affecting the
levels of proteins in the body…

Figure 1: The first few sentences of the abstract and lay summary of an eLife
article, illustrating differences in the language and focus on background
information.

This latter point is especially important for biomedical research which, in
addition to having particularly dynamic and confusing terminology (Smith,
2006; Peng et al., 2021), has the potential to directly impact people’s
decision-making regarding health-related issues, with a pertinent example of
this being the widespread misinformation seen during the COVID-19 pandemic
(Islam et al., 2020). Aiming to address these challenges, some academic
journals choose to publish lay summaries that clearly and concisely explain
the context and significance of an article using non-specialist language.
Figure 1 illustrates how simplifying jargon (e.g., “SARS-CoV-2" $\rightarrow$
“the virus that causes COVID-19") and focusing on background information
allows a reader to better understand a complex scientific topic. However, in
addition to placing an extra burden on authors, lay summaries are not yet
ubiquitous and focus only on newly published articles.

Automatic text summarisation can provide significant value in the generation
of scientific lay summaries. Although previous use of summarisation techniques
for scientific articles has largely focused on generating a technical summary
(e.g., the abstract), only a few have addressed the task of lay summarisation
and introduced datasets to facilitate its study (Chandrasekaran et al., 2020;
Guo et al., 2021; Zaman et al., 2020). However, compared to datasets
ordinarily used for training supervised summarisation models, these resources
are relatively small (ranging from 572 to 6,695 articles), presenting a
significant barrier to the deployment of large data-driven approaches that
require training on large amounts of parallel data. Furthermore, these
resources are somewhat fragmented in terms of their framing of the task,
making use of article and summary formats that limit their applicability to
broader biomedical literature. These factors hinder the progression of the
field and the development of usable models that can be used to make scientific
content accessible to a wider audience.

To help alleviate these issues, we introduce two new datasets derived from
different academic journals within the biomedical domain \- PLOS and eLife
(§3). Both datasets use the full journal article as the source, enabling the
training of models which can be broadly applied to wider literature. PLOS is
significantly larger than currently available datasets and makes use of short
author-written lay summaries (150-200 words), whereas eLife’s summaries are
approximately twice as long and written by expert editors who are well-
practiced in the simplification of scientific content. Given these differences
in authorship and length, we expect the lay summaries of eLife to simplify
content to a greater extent, meaning our datasets are able to cater to
different audiences and applications (e.g., personalised lay summarisation).
We confirm this via an in-depth characterisation of the lay summaries within
each dataset, quantifying ways in which they differ from the technical
abstract and from each other (§4). Finally, we benchmark our datasets with
popular summarisation approaches using automatic metrics and conduct an
expert-based manual evaluation, highlighting the utility of our datasets and
key challenges for the task of lay summarisation (§5). This paper also
presents a literature review (§2), conclusions (§6), and a discussion on its
limitations (§7).

##  2 Related Work

Past attempts to automatically summarise scientific content in layman’s terms
have been scarce, with the most prominent example being the LaySumm subtask of
the CL-SciSumm 2020 shared task series (Chandrasekaran et al., 2020) which
attracted a total of 8 submissions. Alongside the task, a training corpus of
572 articles and author-generated lay summaries from a multi-disciplinary
collection of Elsevier-published scientific journals was provided, with
submissions being evaluated on a blind test set of 37 articles. It was noted
by the task organisers that the data provided was insufficient for training a
model to produce a realistic lay summary.

Guo et al. (2021) also make use of a single publication source to retrieve lay
summaries: The Cochrane Database of Systematic Reviews (CDSR). Their dataset
contains the abstracts of 6,695 systematic reviews paired with their
respective plain-language summaries, covering various healthcare domains.
Although larger than other available datasets for lay summarisation, CDSR is
constrained in that it only uses the abstracts of systematic reviews as source
documents, and thus models trained using CDSR will be unlikely to generalise
well to inputs that are longer than an abstract or the abstracts of other
types of publication.

Alternatively, Zaman et al. (2020) introduce a dataset derived from the
‘Eureka-Alert’ science news website for the combined tasks of simplification
and summarisation. Summaries consist of news articles (average length > 600
words) that aim to describe the content of a scientific publication to the
non-expert. However, the extensive size of reference summaries is likely to
present additional challenges in model training and their news-based format
limits their applicability (e.g., in automating lay summarisation for
journals).

Compared to previous resources, our datasets contain articles and lay
summaries of a format that we consider to be more broadly applicable to wider
literature. Additionally, PLOS is significantly larger than those currently
available (over 4$\times$ larger than CDSR) and eLife contains summaries
written by expert editors. Furthermore, our work is the first to provide two
datasets with different levels of readability, thus supporting the needs of
different audiences and applications. Through each of these factors, we hope
to enable the creation of more usable lay summarisation models.

##  3 Our Datasets

We introduce two datasets from different biomedical journals (PLOS and eLife),
each containing full scientific articles paired with manually-created lay
summaries. For each data source, articles were retrieved in XML format and
parsed using Python to retrieve the lay summary, abstract, and article
text.111For each article, we also retrieve a number of keywords from the meta-
data, providing an indication of the high-level topics covered within the
article. In line with previous datasets for scientific summarisation (Cohan et
al., 2018), the article text is separated into sections, and the heading of
each section is also retrieved. Sentences are segmented using the PySBD rule-
based parser (Sadvilkar and Neumann, 2020), which we empirically found to
outperform neural alternatives. We separate our datasets into training,
validation, and testing splits at a ratio of 90%/5%/5%. Statistics describing
the contents of our datasets and that of past lay summarisation datasets are
given in Table 1.

Dataset | # Docs | Doc | Summary
---|---|---|---
# words | # words | # sents
LaySumm | 572 | 4,426.1 | 82.15 | 3.8
Eureka-Alert | 5,204 | 5,027.0 | 635.6 | 24.3
CDSR | 6,695 | 576.0 | 338.2 | 16.1
1-5[1.5pt/2pt] PLOS | 27,525 | 5,366.7 | 175.6 | 7.8
eLife | 4,828 | 7,806.1 | 347.6 | 15.7

Table 1: Statistics of lay summarisation datasets, with ours given in bold.
Words and sentences (sents) are average values.

#### PLOS

The Public Library of Science (PLOS) is an open-access publisher that hosts
influential peer-reviewed journals across all areas of science and medicine.
Several of these journals require authors to submit an author summary
alongside their work, defined as a 150-200 word non-technical summary aimed at
making the findings of a paper accessible to a wider audience, including non-
scientists.222Source of PLOS author summary definition:
https://journals.plos.org/plosgenetics/s/submission-guidelines The journals in
question focus specifically on Biology, Computational Biology, Genetics,
Pathogens, and Neglected Tropical Diseases.

#### eLife

eLife is an open-access peer-reviewed journal with a specific focus on
biomedical and life sciences. Of the articles published in eLife, some are
selected to be the subject of a digest, a simplified summary of the work
written by expert editors based on both the article itself and questions
answered by its author. Similarly to PLOS, these digests aim to explain the
background and significance of a scientific article in language that is
accessible to non-experts (King et al., 2017).

Metric | Abstract |  | Lay Summary
---|---|---|---
PLOS | eLife |  | PLOS | eLife
FKGL$\downarrow$ | 15.04 | 15.57 |  | 14.76 | 10.92
CLI$\downarrow$ | 16.39 | 17.68 |  | 15.90 | 12.51
DCRS$\downarrow$ | 11.06 | 11.78 |  | 10.91 | 8.83
WordRank$\downarrow$ | 9.08 | 9.21 |  | 8.98 | 8.68

Table 2: Mean readability scores for abstracts and lay summaries from our
datasets. For all metrics, a lower score indicates greater readability.

##  4 Dataset Analysis

We carry out several analyses comparing the lay summaries of our datasets to
the respective technical abstracts. Through these analyses, we seek to
highlight and quantify the key differences between these two different types
of summary, as well as those present between the lay summaries of our two
datasets. Specifically, we focus on readability (§4.1), rhetorical structure
(§4.2), vocabulary sharing (§4.3), and abstractiveness (§4.4).

Figure 2: Barplot visualising the rhetorical class distributions in our
abstracts and lay summaries.

###  4.1 Readability

We assess the readability of our lay summaries and abstracts using several
established metrics. Specifically, we employ Flesch-Kincaid Grade Level
(FKGL), Coleman-Liau Index (CLI), Dale-Chall Readability Score (DCRS), and
WordRank score.333Computed using the textstat and EASSE (Alva-Manchego et al.,
2019) packages. FKGL, CLI, and DCRS provide an approximation of the (US) grade
level of education required to read a given text. The formula for FKGL
surrounds the total number of sentences, words, and syllables present within
the text, whereas CLI is based on the number of sentences, words, and
characters. Alternatively, DCRS measures readability using the average
sentence length and the number of familiar words present, using a lookup table
of the 3,000 most commonly used English words. Similarly, WordRank estimates
the lexical complexity of a text based on how common the language is, using a
frequency table derived from English Wikipedia.

The scores given in Table 2 show that the lay summaries of both datasets are
consistently more readable than their respective abstracts across all metrics.
Although these differences are small in some cases, in line with the findings
of previous works (Devaraj et al., 2021), we find them all to be statistically
significant by way of Mann–Whitney U tests ($p<0.05$). These results indicate
that lay summaries are more readable than technical abstracts in terms of both
syntactic structure and lexical intelligibility. Additionally, the lay
summaries from eLife obtain lower readability scores than those of PLOS across
all metrics, confirming our expectation that they are suitable for less
technical audiences.444Manual inspection of the summaries from each dataset
also support this.

Label | Abstract |  | Lay Summary
---|---|---|---
PLOS | eLife |  | PLOS | eLife
Background | $35.40$ | $41.05$ |  | $58.11$ | $55.03$
Objective | $0.76$ | $1.06$ |  | $0.54$ | $0.47$
Methods | $10.26$ | $6.73$ |  | $6.24$ | $6.23$
Results | $34.75$ | $30.60$ |  | $17.86$ | $18.23$
Conclusions | $18.83$ | $20.55$ |  | $17.26$ | $18.83$

Table 3: Mean percentage of each rhetorical label within our abstracts and lay
summaries.

Figure 3: Stacked barplot showing how regularly (on average) abstract content
words are shared with the respective lay summaries (as a % of all words of
that type), separated by number of abstract occurrences.

###  4.2 Rhetorical Structure

Rhetoric is another important factor when assessing the comprehensibility of a
text. Specifically, a lay person will require a much larger focus on the
background of a scientific article than an expert in order to understand the
significance of its findings (King et al., 2017), thus we would expect lay
summaries to focus more on such aspects.

To provide further insight into the structural differences between abstracts
and lay summaries, we classify all sentences within each based on their
rhetorical status. To do this, we make use of PubMed RTC (Dernoncourt and Lee,
2017), a dataset containing the 20,000 biomedical abstracts retrieved from
PubMed, with each sentence labelled according to its rhetorical role (roles:
Background, Objective, Methods, Results, Conclusions). We use PubMed RTC to
train the BERT-based sequential classifier introduced by Cohan et al. (2019)
due to its strong reported performance (92.9 micro F1-score), before applying
this model to lay summary and abstract sentences from our datasets.

Figure 2 provides a visualisation of how the frequency of each rhetorical
class changes according to the sentence position within our summaries. For
each sub-graph, observing the pattern of most frequent labels (tallest bars)
across all positions allows us to get an idea of the dominant rhetorical
structure. In Table 3, we further quantify the difference in structure by
giving the average percentage of each label present in the different
summaries.

For both datasets, we see a similar pattern when comparing abstract and lay
summary distributions. Specifically, a much greater portion of lay summary
sentences is dedicated to explaining the relevant background information
(“Background"). This is unsurprising, as such information is essential to
understanding the motivation and significance of any work and, thus, would be
of great value to a non-expert. This additional focus on “Background" comes at
the expense of sentences focusing on “Results" and (to a lesser extent)
"Methods", which are less frequent within lay summaries. Again, this is to be
expected, as these details are less meaningful to an audience without domain
expertise.

###  4.3 Content Words

Aiming to determine what terminology is shared between summary types, we
analyse the frequency at which content words occur simultaneously within
abstracts and lay summaries. We treat nouns, proper nouns, verbs, and numbers
as content words, and we extract these from the summaries using ScispaCy
(Neumann et al., 2019), a library that specialises in the processing of
biomedical texts.555en_core_sci_scibert model used for POS tagging, derived
from SciBERT (Beltagy et al., 2019). Figure 3 shows the results of our
analysis, visualising the average rate at which different types of content
word from the abstracts are shared with lay summaries. We divide our analysis
based on the number of abstracts content words occur in, allowing us to
observe how the ubiquity of a word within the corpus affects the rate at which
it is shared. 666E.g., For nouns within PLOS, 15.3% occur in a single abstract
and are shared with lay summaries, and 48.2% occur in a single abstract and
are not shared (i.e., total percentage of nouns that occur in a single
abstract = 15.3 + 48.2 = 63.5%).

Generally, we observe similar patterns for content words between datasets.
Firstly, regardless of word type or number of abstract occurrences, we find
that abstract content words are rarely shared with lay summaries (i.e.,
‘shared’ % < ‘not shared’ % for all bars). This is indicative of a clear shift
in content and/or vocabulary when it comes to the creation of a lay summary.
For each dataset, we can also see that the vast majority of content words of
all types, except verbs, occur in 10 or fewer abstracts (> 90% on average for
both datasets), with most of these occurring within a single abstract. We
found that the majority of these low-frequency content words are highly
specific to the topic of a single article or small group of articles. In the
case of nouns and proper nouns, we empirically observed these instances to
often be highly technical terms (e.g., specific chemicals, lesser-known
diseases, etc.), whereas numbers are typically exact numerical figures. It is
understandable, therefore, that single-use content words of these types (noun,
proper noun, and number) are rarely included in the lay summary, as they will
likely be meaningless to a lay reader. The pattern exhibited by verbs differs
significantly from that of other word types, as they typically occur in a
greater number of abstracts (most commonly being present within 2-10). For
content words of all types, we observe that the ratio of ‘shared’ to ‘not
shared’ generally increases in line with the number of abstract
occurrences.777Better illustrated by Table 7 in Appendix A, which gives the
exact percentages shown in Figure 3.

###  4.4 Abstractiveness

We follow the example of prior works (Sharma et al., 2019; See et al., 2017)
by calculating the abstractiveness of our summaries using $n$-gram novelty,
thus providing a measurement of the degree to which the summary uses different
language to describe the content of the article. Specifically, for both
abstracts and lay summaries, we compute the percentage of summary $n$-grams
which are absent from their respective article. The results of this analysis
are presented in Figure 4, where we can observe that lay summaries
consistently contain more novel $n$-grams than abstracts across both datasets.
However, the lay summaries of eLife, in addition to being approximately twice
as long as those of PLOS (Table 1), appear to be significantly more
abstractive. Alongside differences in readability (highlighted in §4.1), we
believe these to be important distinctions that should be considered in
determining a suitable dataset for a particular use case or application.

Figure 4: Barplot showing the percentage of novel $n$-grams for each summary
type.

##  5 Experiments and Results

To help facilitate future work, we benchmark our datasets using popular
heuristics-based, unsupervised, and supervised summarisation approaches
(§5.1). Additionally, we provide further insight into these results via a
detailed discussion (§5.2) and an expert-based manual evaluation (§5.3).

Approach |  | PLOS |  | eLife
---|---|---|---|---
| R-1 | R-2 | R-L | FKGL | DCSR |  | R-1 | R-2 | R-L | FKGL | DCSR
Heuristic Lead-3 |  | 25.46 | 6.35 | 22.89 | 15.08 | 12.66 |  | 17.93 | 3.66 | 16.45 | 13.30 | 12.65
Lead-K |  | 35.50 | 8.69 | 32.33 | 14.94 | 11.88 |  | 34.12 | 6.73 | 32.06 | 11.89 | 10.58
Abstract |  | 47.07 | 18.60 | 43.51 | 14.98 | 11.10 |  | 28.92 | 6.19 | 27.04 | 15.35 | 11.87
Oracle_Ext |  | 56.43 | 31.24 | 52.88 | 15.28 | 11.20 |  | 46.38 | 11.48 | 43.82 | 13.18 | 10.51
Unsupervised LSA |  | 35.14 | 6.92 | 31.46 | 17.39 | 12.41 |  | 34.57 | 5.69 | 32.27 | 16.50 | 11.52
LexRank |  | 36.53 | 9.43 | 33.09 | 14.90 | 10.51 |  | 30.32 | 5.29 | 28.40 | 13.45 | 9.75
TextRank |  | 36.03 | 8.71 | 32.04 | 20.42 | 11.83 |  | 31.45 | 5.28 | 29.22 | 19.13 | 11.28
HipoRank |  | 42.16 | 11.41 | 38.01 | 14.91 | 11.82 |  | 29.95 | 5.38 | 27.44 | 13.58 | 12.32
Supervised Bart |  | 42.35 | 12.96 | 38.57 | 14.62 | 12.07 |  | 46.57 | 11.65 | 43.70 | 10.94 | 9.36
BartPubMed |  | 42.12 | 12.70 | 38.34 | 14.75 | 12.09 |  | 46.20 | 11.36 | 43.21 | 11.24 | 9.64
BartCross |  | 42.24 | 13.52 | 38.63 | 14.24 | 12.18 |  | 46.22 | 11.53 | 43.33 | 10.81 | 9.31
BartScaffold |  | 39.47 | 9.73 | 35.79 | 14.45 | 11.74 |  | 45.28 | 10.99 | 42.51 | 10.65 | 9.19

Table 4: Performance of summarisation models on the test splits of each
dataset (R = average ROUGE F1-score). The best non-heuristic scores for each
metric are given in bold.

###  5.1 Baseline Approaches

For our heuristics-based approaches, we include the widely-used Lead-3
baseline which simply uses the first three sentences of the main body of the
text. As our lay summaries typically consist of more than three sentences, we
also include Lead-k, with $k$ being equal to the average lay summary length
for each dataset (Table 1). Additionally, we include the scores obtained by
the technical abstracts (Abstract) and Oracle_Ext, a greedy extractive oracle
(Nallapati et al., 2017) that provides an upper bound for the expected
performance of extractive models.888The extractive oracle is further explained
in Appendix A.

We benchmark four unsupervised extractive approaches: LSA (Steinberger and
Jezek, 2004), LexRank (Erkan and Radev, 2004), TextRank (Mihalcea and Tarau,
2004), and HipoRank (Dong et al., 2021). For supervised models, we use the
transformer-based BART base model (Lewis et al., 2020), which we fine-tune on
our own datasets. To assess how the use of additional data in various forms
can benefit performance, we include several other variants of this model which
are described in the remainder of this subsection.999For all variants, we
truncate the input to 1,024 tokens.

#### Additional training

As our datasets remain smaller than those used in other forms of
summarisation, we experiment with BARTPubMed, which is previously trained on
the PubMed abstract generation dataset (Cohan et al., 2018) and fine-tuned on
our own datasets. Aiming to assess how well models trained on PLOS can
generalise to eLife and vice versa, we also include BARTCross, which is
initially trained on the opposite dataset to that which it is eventually fine-
tuned and evaluated on.

#### Scaffolding

We also experiment with artificially enlarging our training data by way of a
scaffold task. Inspired by CATTS (Cachola et al., 2020), we remove the
article’s abstract from the input text and train the model to generate the
abstract as a scaffold task to lay summarisation. In addition to showing
whether training for abstract generation can benefit lay summarisation,
results for this model will provide an indication of the baseline BART model’s
reliance on the abstract content. Specifically, we include two copies of every
article within our training data - one using the abstract as the reference
summary and the other using the lay summary. We distinguish between the two by
prepending the input document with the control tokens
$\langle|$ABSTRACT$|\rangle$ or $\langle|$SUMMARY$|\rangle$. Documents within
the validation and test splits are prepended the $\langle|$SUMMARY$|\rangle$
code to induce lay summary generation. This model is denoted by BARTScaffold.

###  5.2 Discussion

Table 4 presents the performance of the aforementioned approaches on the PLOS
and eLife test splits using automatic metrics. In line with common practice
for summarisation, we report the F1-scores of ROUGE-1, 2, and L (Lin, 2004).
Additionally, we include FKGL and DCRS scores of the generated output (see
§4.1), providing an assessment of the syntactic and lexical complexity,
respectively.

#### The importance of the abstract

Based on the ROUGE scores obtained by the Abstract baseline, we can safely
assume that the lay summaries of PLOS are much closer in resemblance to their
respective abstracts than those of eLife. The importance of the abstract for
lay summary generation is further highlighted by the ROUGE scores of the
BARTScaffold model, which performs notably worse than the standard BART model
on PLOS and slightly worse on eLife. These results suggest that having the
abstract included within the model input provides significantly more benefit
than using abstract generation as an auxiliary training signal.

#### Extractive vs abstractive

In general, we would expect abstractive methods to have greater application
for the task of lay summarisation due to their ability to transform (and thus,
simplify) an input text. However, abstractive approaches have a tendency to
generate hallucinations, resulting in factual inconsistencies between the
source and output that damages their usability (Maynez et al., 2020).
Therefore, extractive approaches may still have utility for the task,
especially if the comprehensibility of selected sentences is directly
considered.

For ROUGE scores, we find that extractive baselines (i.e., all unsupervised
and heuristic approaches) perform significantly better on PLOS than on eLife,
aligning with our previous analysis (§4.4) which identified PLOS as the less
abstractive dataset. Interestingly, readability scores achieved by extractive
models on PLOS match and sometimes exceed those of abstractive BART models,
although they are inconsistent. For eLife, abstractive methods (i.e., BART
models) generally obtain superior scores for both ROUGE and readability
metrics. In fact, the ROUGE scores achieved by BART exceed those obtained by
Oracle_Ext, further indicating that abstractive methods have greater potential
for this dataset.

#### Use of additional data

As previously mentioned, artificially creating more data via an abstract-
generation scaffold task results in a decrease in ROUGE scores for both
datasets, indicating a reliance on the abstract content for lay summarisation.
We also find that pretraining BART on Pubmed (BARTPubMed) does very little to
affect the performance, suggesting that habits learned for abstract generation
do not transfer well to lay summarisation. Similarly, BARTCross achieves a
performance close to that of the standard BART model. Overall, these results
indicate that additional out-of-domain training does provide much benefit for
lay summarisation, and alternative modelling approaches that make better use
of available data may be a more promising route for future work.

###  5.3 Human evaluation

To further assess the usability of the generated abstractive summaries, we
perform an additional human evaluation of our standard Bart baseline model
using two domain experts.101010Both judges have experience in scientific
research and hold at least a bachelor’s degree in Biomedical Science.

Our evaluation uses a random sample of 10 articles from the test split of each
dataset. Alongside each model-generated summary, judges are presented with
both the abstract and reference lay summary of the given article. Using a 1-5
Likert scale, the annotators are asked to rate the model output based on three
criteria: (1) Comprehensiveness – to what extent does the model output contain
all information that might be necessary for a non-expert to understand the
high-level topic of the article and the significance of the research; (2)
Layness – to what extent is the content of the model output comprehensible (or
readable) to a non-expert, in terms of both structure and language; (3)
Factuality – to what extent is the model output factually consistent with the
two other provided summaries. We choose not to provide judges with the full
article text in an effort to minimise the complexity of the evaluation and the
cognitive burden placed upon them.

Dataset | Comp. | Layness | Factuality
---|---|---|---
PLOS | 3.7 | 3.0 | 3.0
eLife | 3.1 | 3.0 | 3.0
Table 5: Mean evaluator ratings (1-5) obtained by Bart outputs for each
metric.

Table 5 presents the average ratings from our manual evaluation. We calculate
Krippendorff’s $\alpha$ to measure inter-rater reliability, where we obtain
values of 0.78 and 0.54 for PLOS and eLife, respectively. In addition to
providing ratings, evaluators also provided comments on the general
performance on each criterion for both datasets, providing further insights
into model performance.

#### Comprehensiveness

We can see from Table 5 that model outputs on PLOS are judged to be more
comprehensive than on eLife. From evaluators’ comments, we understand that
this largely results from extensive use of abstract content for PLOS, which is
sometimes copied directly (or with minor edits) to the lay summary. For eLife,
it was observed that new information (i.e., not contained in the reference
abstract or lay summary) was often introduced which was irrelevant or
confusing, potentially affecting the understanding of a lay reader.

#### Layness

Interestingly, given the previously highlighted differences in readability,
the average layness of the model output is judged to be equal for both
datasets (3.0), suggesting a reasonable degree of content simplification.
However, evaluators’ comments indicate that model outputs for each dataset
were penalised for different reasons. For PLOS, the aforementioned use of
abstract content often resulted in the inclusion of jargon terms that a lay
reader would struggle to interpret. Alternatively, the language of eLife
outputs was observed to be better suited to a lay audience but was sometimes
simplified to a point that it could be misconstrued and mislead a reader,
occasionally containing grammatical errors, typos, and repeated content.

#### Factuality

Again, we find an equal average score of 3.0 given for factuality, suggesting
the model struggles to produce factually correct outputs for both datasets. In
fact, we found no output from either dataset was given a perfect score by both
annotators, indicating that simplifying technical content accurately is a
consistent problem. Evaluators’ comments highlight contradictions, unclear
phrasing, and misrepresentation of entities as key contributing factors to
factual inconsistencies. We regard this as an integral obstacle to overcome in
the development of usable lay summarisation models and an essential focus for
future research.

##  6 Conclusion

In this work, we have introduced PLOS and eLife, two new datasets for the lay
summarisation of biomedical research articles. Compared to currently available
resources, these datasets possess source article and summary formats that are
more broadly applicable to wider literature, with PLOS also being larger by a
significant margin. A thorough analysis of our lay summaries highlights key
differences between datasets, enabling them to cater to the needs of different
audiences and applications. Specifically, in addition to being approximately
twice as long as those of PLOS, we find eLife summaries to be both more
readable and abstractive, thus better suited to a less technical audience. To
facilitate future research, we benchmark our datasets with popular
summarisation models using automatic metrics and conduct an expert-based human
evaluation, providing further insight into the intricacies of model
performance on our datasets and highlighting key challenges for the task of
lay summarisation.

##  7 Limitations

Although we introduce the largest dataset available to the task of lay
summarisation, our datasets remain smaller than those available for other
forms of summarisation (e.g., abstract generation), where there exists
datasets containing 100,000+ articles. This is largely due to the fact that
lay summaries are less ubiquitous that other forms of summary (e.g., the
abstract), only being used in a relatively small number of journals, of which
only some are open-access and available to be utilised for such purposes as
dataset creation.

On a related note, another potential limitation of our datasets is the fact
they only cover a single broad domain - biomedicine. Again, this comes down to
the availability of data, and the fact that the use of lay summaries is much
less common in other scientific domains (e.g. Computer Science). There is,
however, a reason for this disparity in the adoption of lay summaries between
domains, as it is generally considered more important that the public have an
awareness and understanding of research breakthroughs in health-related areas
such as biomedicine. Therefore, we believe it is in these domains that
automatic lay summarisation can provide the greatest benefit, although we also
hope to address the lay summarisation of other domains in future work.

##  8 Acknowledgements

This work was supported by the Centre for Doctoral Training in Speech and
Language Technologies (SLT) and their Applications funded by UK Research and
Innovation [grant number EP/S023062/1].

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##  Appendix A Appendix

#### Availability of data sources

Both PLOS and eLife are open-access journals, with an emphasis on making
scientific research accessible to a wide audience. PLOS articles are available
to be mined, reused, and shared by anyone, as per their data mining
policy.111111https://plos.org/text-and-data-mining eLife articles are
available under the permissive CC-BY 4.0 license, and thus also available to
be retrieved and shared for these
purposes.121212https://elifesciences.org/inside-elife/6933fe8e/resources-for-
developers The data for PLOS and eLife was retrieved on 7/03/22 and 11/03/22,
respectively. All articles and summaries are in English only. Our datasets are
made available to the community to facilitate future research.

#### Additional data processing details

Here we provide some additional details regarding the dataset creation
process, building on the description given in §3. For eLife, the XML files
retrieved were found to include multiple versions of the same article,
identifiable by the article id which includes a version number. For these, we
removed duplicates and kept only the most recent versions.

For both datasets, prior to extraction, we remove all Tables, Figures, and
sections marked with the tag “supplementary-material". We also do not extract
sections with the heading “acknowledgments". During sentence segmentation, we
use a regular expression to identify and temporarily replace all "et al."
occurrences with unique placeholder tokens, which are then replaced following
segmentation. Following segmentation, we again use a regular expression to
identify and remove all sentences which began with “DOI:" followed by a URL
from both abstracts and lay summaries, as these were found to commonly occur
at the end of both.

#### Comparison to previous datasets

We provide a comparison of the readability and abstractiveness of lay
summaries for all lay summarisation datasets in Table 6 and Figure 5,
respectively.

#### Supplementary figure details

Table 7 gives the exact percentages that are visualised in Figure 3, allowing
for a more detailed analysis of content word sharing (e.g., calculating the
ratio ‘shared’ to ‘not shared’ for different content word types).

#### Baseline model details

Here we provide additional experimental details for our baselines approaches
(Table 4). Oracle_Ext is a greedy oracle, which means it repeatedly extracts
the next article sentence that will maximise the mean ROUGE scores (1, 2, and
L) of the extracted summary, up to the maximum length (equal to the average
lay summary length for a given dataset - Table 1).

For all BART models, we make use of the huggingface library (Wolf et al.,
2019). Specifically, we use the “facebook/bart-base" model for baselines BART,
BARTCross, and BARTScaffold, and we use the “mse30/bart-base-finetuned-pubmed"
model for BARTPubMed. Training was run (using 4x NVIDIA Tesla V100 SXM2 GPUs)
for all models with AdamW optimisation (Loshchilov and Hutter, 2019) and an
early stopping patience of 25 epochs, with the best model being selected by
performance on the validation set (ROUGE-2).

All unsupervised baselines were run with default configurations.

#### Automatic evaluation

For the calculation of ROUGE scores, we use the rouge-score Python
package.131313https://pypi.org/project/rouge-score/ For FKGL and DCSR, we use
the textstat Python package.141414https://github.com/shivam5992/textstat

#### Human evaluation comments

Comments on the general model performance for each criterion provided by each
annotator for our human evaluation are given in Figures 6 and 7 for PLOS and
eLife, respectively.

#### Lay summary examples

Full examples of lay summaries and their respective technical abstracts are
given in Figures 8 and 9 for PLOS and eLife, respectively.

Dataset | FKGL | CLI | DCRS | WordRank
---|---|---|---|---
LaySumm | $14.81_{\pm 2.91}$ | $16.28_{\pm 2.81}$ | $11.63_{\pm 1.16}$ | $9.04_{\pm 0.60}$
Eureka-Alert | $13.16_{\pm 2.07}$ | $14.09_{\pm 1.56}$ | $9.77_{\pm 0.85}$ | $8.79_{\pm 0.80}$
CDSR | $12.72_{\pm 2.16}$ | $14.17_{\pm 0.88}$ | $9.073_{\pm 0.88}$ | $8.54_{\pm 0.32}$
PLOS | $14.76_{\pm 2.33}$ | $15.90_{\pm 2.01}$ | $10.91_{\pm 0.85}$ | $8.98_{\pm 0.32}$
eLife | $10.91_{\pm 1.44}$ | $12.52_{\pm 1.35}$ | $8.94_{\pm 0.53}$ | $8.68_{\pm 0.31}$
Table 6: Comparison of the lay summary readability scores for all lay
summarisation datasets.

Figure 5: Comparison of lay summary $n$-gram novelty for all lay summarisation datasets. Word type |  | # of abstract occurrences
---|---|---
| 1 | 2-10 | 11-100 | 100+
PLOS Noun |  | 15.3 / 48.2 | 8.7 / 18.1 | 2.5 / 4.9 | 0.8 / 1.5
Proper noun |  | 15.3 / 54.2 | 8.2 / 18.0 | 1.9 / 2.9 | 0.2 / 0.3
Number |  | 6.3 / 67.6 | 2.4 / 18.6 | 0.5 / 3.8 | 0.2 / 0.6
Verb |  | 4.2 / 28.9 | 4.9 / 29.7 | 3.4 / 18.9 | 1.8 / 8.3
eLife Noun |  | 14.9 / 46.2 | 8.6 / 20.3 | 2.5 / 6.2 | 0.5 / 0.9
Proper noun |  | 19.2 / 64.1 | 6.4 / 9.6 | 0.3 / 0.4 | 0.0 / 0.0
Number |  | 7.6 / 67.6 | 3.8 / 16.8 | 1.2 / 2.6 | 0.2 / 0.2
Verb |  | 3.8 / 32.7 | 4.5 / 36.0 | 2.6 / 17.2 | 0.6 / 2.6
Table 7: Statistics for bars in Figure 3. For each table cell, the overall
percentage is split into ‘% that are shared with lay summaries’ / ‘% that are
not shared with lay summaries’.

Comprehensiveness Annotator 1: The model outputs summarised the important
information, however it also cut out quite a lot of background info which is
key for understanding the science. Overall, the comprehensiveness was enough
for a non-expert to grasp the overall gist of the studies. Annotator 2: The
model seems to use parts of the abstract, and therefore seems quite
comprehensive. It also does a decent job of a final “summary" sentence to the
lay summary to summarize/put into context. Layness Annotator 1: Some abstracts
contained a lot of jargon which would be confusing/off putting to a non-
expert. Although I know some scientific words cannot be substituted, it would
be good to have an explanation of the more complex words in brackets, for
example. Annotator 2: Due to the overlap with the reference abstract, the
output is comprehensive but probably confusing to a lay audience, in some
cases there is no introduction/background on scientific jargon (e.g. we cannot
expect a lay audience to understand complex scientific techniques, cellular or
molecular machinery). Use of Genus species nomenclature is also likely to
confuse lay audiences, where a common name could be used instead, not as well
as (e.g. ‘Egyptian mosquito’ instead of ‘Aedes aegypti’, also known as the
Egyptian mosquito’). Factuality Annotator 1: The majority of the statements
were factually correct, although sometimes the meaning of the simplified
language could be misinterpreted, which would result in a similar outcome to
factually wrong statements. Annotator 2: Some minor factual errors throughout,
and mixups between gene symbols (e.g. where one letter will be changed, PMN to
PMA), there are also come cases where it will pull out a % but mix up what
gene / condition it is related to, ultimately leading to the formation of a
sentence which is factually incorrect.

Figure 6: Human evaluation comments for PLOS.

Comprehensiveness Annotator 1: Overall the information contained within the
model-generated summaries effectively conveyed the information in the
references. However, there was a few occasions where new elements/concepts
were introduced that could confuse the reader and affect their understanding
(these were sometimems factual statements, sometimes seemingly made up). The
model abstract did provide enough information for a general understanding of
the topic and would be sufficient as a brief overview. Annotator 2: The model
usually picks up on the core points of the abstract but can often introduce
extra information which is either off-topic or factually incorrect. The model
seems to start off by introducing the topic well but struggles to hit the
“what is the significance of this research?" question. Layness Annotator 1:
The language was, in the majority, well suited to a lay person and terminology
was adapted accordingly. However, at times the information was simplified to a
point where it could be misconstrued which, with scientific information, is a
potential risk. At times, jargon still remained and I could imagine some
people being confused by this. There were a few grammatical errors and poor
sentence structure, typos, repetition etc. Annotator 2: Sometimes the model
introduces extra information which is not suitable for a lay audience, for
example: references to genome sequencing, progenitor cells, endoplasmid
reticulum. There are also instances where misinterpretation by the model may
mislead the lay audience, for example there was an output where Norepinephrine
was said to be “a.k.a. dopamine", which is not factually correct. Factuality
Annotator 1: There were a few summaries which contained incorrect information,
things that are well-known in the scientific community were poorly conveyed.
At times, new information was introduced which contradicted earlier
statements, and those of the reference abstracts/lay summaries. Of course,
some information was correct. I would be concerned about the level of
misinformation which could arise from these summaries, if used to educate a
lay audience. Annotator 2: This seemed to vary based on the abstract and how
well the output started, for example if the model introduced the topic well,
it would lead to more factual points. However, there were some generated
summaries which were factually incorrect from the start and this lead to more
errors.

Figure 7: Human evaluation comments for eLife.

Technical Abstract Rabies is a uniformly fatal disease, but preventable by
timely and correct use of post exposure prophylaxis (PEP). Unfortunately, many
health care facilities in Pakistan do not carry modern life-saving vaccines
and rabies immunoglobulin (RIG), assuming them to be prohibitively expensive
and unsafe. Consequently, Emergency Department (ED) health care professionals
remain untrained in its application and refer patients out to other hospitals.
The conventional Essen regimen requires five vials of cell culture vaccine
(CCV) per patient, whereas Thai Red Cross intradermal (TRC-id) regimen
requires only one vial per patient, and gives equal seroconversion as compared
with Essen regimen. This study documents the cost savings in using the Thai
Red Cross intradermal regimen with cell culture vaccine instead of the
customary 5-dose Essen intramuscular regimen for eligible bite victims. All
patients presenting to the Indus Hospital ED between July 2013 to June 2014
with animal bites received WHO recommended PEP. WHO Category 2 bites received
intradermal vaccine alone, while Category 3 victims received vaccine plus
wound infiltration with Equine RIG. Patients were counseled, and subsequent
doses of the vaccine administered on days 3, 7 and 28. Throughput of cases,
consumption utilization of vaccine and ERIG and the cost per patient were
recorded. Government hospitals in Pakistan are generally underfinanced and
cannot afford treatment of the enormous burden of dog bite victims. Hence,
patients are either not treated at all, or asked to purchase their own
vaccine, which most cannot afford, resulting in neglect and high incidence of
rabies deaths. TRC-id regimen reduced the cost of vaccine to 1/5th of Essen
regimen and is strongly recommended for institutions with large throughput.
Training ED staff would save lives through a safe, effective and affordable
technique. Lay Summary Rabies is a killer disease caused by the rabies virus
that is present in the saliva of rabid animals, mainly the dog. Once symptoms
become apparent, death is inevitable. However, rabies can be prevented if
correct post exposure prophylaxis (PEP) is instituted as soon as possible
after a dog bite and before symptoms of rabies begin. In Pakistan, government
hospitals treat 50-70 new bite victims each day. Many still dispense the free
but poor quality nerve tissue vaccine that is often ineffective and fraught
with serious adverse reactions. Hospital administrators consider PEP too
expensive to be administered free of cost. The Indus Hospital (TIH), Karachi
is a private teaching hospital which provides free medical care to all. From
July 2013-June 2014, 2,983 new bites were seen in the ED, and rather than use
the Essen regimen of five full vial intramuscular doses per patient over 28
days, we administered the WHO-approved Thai Red Cross-intradermal (TRC-id)
4-dose regimen. The use of the TRC-id regimen resulted in 80% cost savings
over the Essen regimen. In resource-poor settings, we advocate training of ED
personnel in TRC-id regimen, which, ultimately, will result in less vaccine
consumption, more patient compliance and complete treatment, resulting in more
lives being saved.

Figure 8: PLOS lay summary example.

Technical Abstract Adult stem cells are responsible for life-long tissue
maintenance. They reside in and interact with specialized tissue
microenvironments (niches). Using murine hair follicle as a model, we show
that when junctional perturbations in the niche disrupt barrier function,
adjacent stem cells dramatically change their transcriptome independent of
bacterial invasion and become capable of directly signaling to and recruiting
immune cells. Additionally, these stem cells elevate cell cycle transcripts
which reduce their quiescence threshold, enabling them to selectively
proliferate within this microenvironment of immune distress cues. However,
rather than mobilizing to fuel new tissue regeneration, these ectopically
proliferative stem cells remain within their niche to contain the breach.
Together, our findings expose a potential communication relay system that
operates from the niche to the stem cells to the immune system and back. The
repurposing of proliferation by these stem cells patch the breached barrier,
stoke the immune response and restore niche integrity. Lay Summary Most, if
not all, tissues of an adult animal contain stem cells. These stem cells
regenerate and repair damaged tissues and organs for the entire lifetime of an
animal, contributing to a healthy life. They divide to make daughter cells
that become either new stem cells or specialized cells of that organ. Adult
stem cells exist in specific areas within tissues known as niches, where they
interact with surrounding cells and molecules that inform their behavior. For
example, cells and molecules within these niches can signal stem cells to
remain in a ‘dormant’ state, but upon injury, they can mobilize stem cells to
form new tissue and repair the wound. So far, it has been unclear how stem
cells sense damage and stress and direct their efforts away from their normal
duties towards repair. Here, Lay et al. studied the stem cells in the mouse
skin that are responsible to regenerate hair. Every hair follicle contains a
niche (the ‘bulge’), where these stem cells live and share their environment
with cells that anchor the hair. The niche tethers to the stem cells through
specific adhesion molecules that also help the niche to form a tight seal to
prevent bacteria from entering. Lay et al. removed one of the adhesion
molecules called E-cadherin, which caused a breach in the niche’s barrier. The
stem cells sensed their damaged niche, prepared to multiply, and sent out
stress signals to the immune system. The immune cells then arrived at the
niche and sent signals back to the stem cells, prodding them to multiply and
patch the barrier, while at the same time, keeping the inflammation in check.
This remarkable ability of the stem cells to recruit immune cells and initiate
a dialogue with them enabled the stem cells to divert their attention from
regenerating hair and instead directing it towards the site of the tissue
damage. Other stem cells, such as those in the lung or gut, may have similar
mechanisms to detect and respond to physical damage. It will be interesting to
investigate the underlying mechanism of how immune cells are involved in
balancing stem cell regenerative capacity and response to physical damage. A
better knowledge of these processes could help to regenerate tissues or even
entire organs.

Figure 9: eLife lay summary example.