mlsanityData to 13 Oct 2026

Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles

arXiv 2309.17332 ↗First posted 29 Sep 2023cs.CLcited by 6

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Abstract (arXiv)

This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023. The goal of this shared task is to develop abstractive summarisation models capable of generating "lay summaries" (i.e., summaries that are comprehensible to non-technical audiences) in both a controllable and non-controllable setting. There are two subtasks: 1) Lay Summarisation, where the goal is for participants to build models for lay summary generation only, given the full article text and the corresponding abstract as input; and 2) Readability-controlled Summarisation, where the goal is for participants to train models to generate both the technical abstract and the lay summary, given an article's main text as input. In addition to overall results, we report on the setup and insights from the BioLaySumm shared task, which attracted a total of 20 participating teams across both subtasks.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 29 Sep 2023.

Builds on

18 of 32 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,884
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,480
  4. 2020Longformer: The Long-Document Transformercited by 3,393
  5. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  6. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 720
  7. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 580
  8. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 536
  9. 2020CORD-19: The COVID-19 Open Research Datasetcited by 179
  10. 2021SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarizationcited by 131
  11. 2022BioBART: Pretraining and Evaluation of A Biomedical Generative Language Modelcited by 83
  12. 2021PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarizationcited by 62
6 more
  1. 2022Making Science Simple: Corpora for the Lay Summarisation of Scientific Literaturecited by 54
  2. 2020Automated Lay Language Summarization of Biomedical Scientific Reviewscited by 46
  3. 2022How Far are We from Robust Long Abstractive Summarization?cited by 25
  4. 2022Readability Controllable Biomedical Document Summarizationcited by 19
  5. 2022LSG Attention: Extrapolation of pretrained Transformers to long sequencescited by 16
  6. 2022Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documentscited by 7

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 1.

6 papers.

  1. 2023A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Taskscited by 36
  2. 2024SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generationcited by 12
  3. 2023Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Paperscited by 9
  4. 2025Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptationcited by 5
  5. 2024The use of large language models to enhance cancer clinical trial educational materialscited by 0
  6. 2026Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptationcited by 0