---
title: "Making Science Simple — paper by Goldsack et al. (2022)"
description: "Lay summarisation means condensing a scientific article and rewriting it in plain language so that… 31 references, cited by 75 papers here."
authors: ["Tomas Goldsack", "Zhihao Zhang", "Chenghua Lin", "Carolina Scarton"]
year: 2022
venue: "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2022.emnlp-main.724/"
doi: "10.18653/v1/2022.emnlp-main.724"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/goldsack-2022-making-science-simple-corpora-lay/"
---
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# Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature

**Authors:** Tomas Goldsack, Zhihao Zhang, [Chenghua Lin](https://md.research.elamur.ai/free/en/authors/chenghua-lin/) and [Carolina Scarton](https://md.research.elamur.ai/free/en/authors/carolina-scarton/) · **Year:** 2022 · **Venue:** [Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2022](https://md.research.elamur.ai/free/en/venues/emnlp/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.emnlp-main.724/) · [PDF](https://aclanthology.org/2022.emnlp-main.724.pdf) · [DOI 10.18653/v1/2022.emnlp-main.724](https://doi.org/10.18653/v1/2022.emnlp-main.724) · [OpenAlex](https://openalex.org/W4385573391)

**Citations:** 40 citations (OpenAlex, 2026-10-14) · [75](https://md.research.elamur.ai/free/en/papers/goldsack-2022-making-science-simple-corpora-lay/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Lay summarisation means condensing a scientific article and rewriting it in plain language so that non-specialists can follow it. Before this paper, the corpora for the task were small (572 to 6,695 items). They also used formats that do not carry over well to general literature, such as abstracts-only sources or long news-style summaries. The authors built two new biomedical datasets that pair full journal articles with lay summaries written by people: PLOS, which is large and uses author-written summaries, and eLife, which is medium-sized and uses digests written by expert editors.

They compared the lay summaries with the technical abstracts on readability, rhetorical structure, shared vocabulary and abstractiveness. eLife summaries came out roughly twice as long, easier to read and more abstractive than PLOS summaries, so each dataset fits a different audience. The authors also benchmarked standard summarisation systems with automatic metrics and an expert manual evaluation. Factual consistency stood out as the main unsolved problem. These resources make data-driven lay summarisation models practical to build.

## Key points
- **Dataset sizes and lengths:**
  - PLOS has 27,525 articles, with sources averaging about 5,367 words and summaries about 176 words.
  - eLife has 4,828 articles, with sources averaging about 7,806 words and summaries about 348 words.
  - PLOS is over four times the size of CDSR.
- **Processing pipeline:**
  - Articles were parsed from XML into the lay summary, the abstract, and sections with their headings, plus keywords from the metadata.
  - Sentences were split with the rule-based PySBD, which beat neural alternatives in the authors' tests.
  - The data is split 90/5/5 into train, validation and test.
- **Readability (lower is easier):**
  - FKGL is about 15 for abstracts in both datasets.
  - PLOS lay summaries barely change (14.76), while eLife lay summaries drop to 10.92.
  - CLI, DCRS and WordRank show the same pattern.
- **Manual evaluation setup:**
  - Two judges with biomedical degrees rated 10 test articles per dataset on a 1–5 Likert scale.
  - The criteria were Comprehensiveness, Layness and Factuality.
  - Krippendorff's α was 0.78 for PLOS and 0.54 for eLife.
- **Manual evaluation results:**
  - Layness and Factuality both averaged 3.0 on each dataset.
  - No single output got a perfect factuality score from both judges.
  - PLOS outputs often reused abstract text, which carried jargon into the summary.
  - eLife outputs sometimes oversimplified to the point of being misleading, or added irrelevant content.

## Interesting ideas
- **Paired datasets at two simplification levels.** Releasing two corpora that differ in how strongly they simplify lets the same task target different reader groups, including personalised summaries. The same design would suit Ukrainian text simplification resources for different audiences.
- **Abstracts as a measurement baseline.** The abstract is an existing technical summary of the same article, so comparing lay summaries against it isolates what simplification actually changes. Running several readability metrics side by side makes that comparison more robust.
- **Judging against short references instead of the full text.** Evaluators saw only the abstract and the reference summary, not the whole paper. This lightens the annotation workload while still allowing factuality checks, which is a useful compromise for any long-document evaluation.
- **Collecting free-text comments alongside Likert scores.** The judges' comments explained why identical average scores arose from different failures, such as copied jargon versus oversimplification. Scores alone would have hidden this.
- **Using a rule-based splitter after an empirical check.** The authors tested PySBD against neural sentence splitters and kept it because it worked better on scientific text. This is a reminder to verify cheap tools before reaching for neural ones in data pipelines.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Beihang University](https://md.research.elamur.ai/free/en/universities/beihang-university/) — University, Beijing, China · 27 works · 8,089 citations

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## Cite

Cite as: Tomas Goldsack, Zhihao Zhang, Chenghua Lin and Carolina Scarton. 2022. Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{goldsack2022making,
  title = {{Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature}},
  author = {Tomas Goldsack, Zhihao Zhang, Chenghua Lin and Carolina Scarton},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year = {2022},
  doi = {10.18653/v1/2022.emnlp-main.724},
  url = {https://doi.org/10.18653/v1/2022.emnlp-main.724}
}
```

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*Updated 24 October 2026*