*BioLaySumm 2023*

*Shared Task*: *Lay Summarization of Biomedical Research Articles @BioNLP Workshop, ACL 2023*

Biomedical publications contain the latest research on prominent health-related topics, ranging from common illnesses to global pandemics. This can often result in their content being of interest to a wide variety of audiences including researchers, medical professionals, journalists, and even members of the public. However, the highly technical and specialist language used within such articles typically makes it difficult for non-expert audiences to understand their contents.

Abstractive summarization models can be used to generate a concise summary of an article, capturing its salient point using words and sentences that aren’t used in the original text. As such, these models have the potential to help broaden access to highly technical documents when trained to generate summaries that are more readable, containing more background information and less technical terminology (i.e., a “lay summary”).

This shared task surrounds the abstractive summarization of biomedical research articles, with an emphasis on controllability and catering to non-expert audiences. Through this task, we aim to help foster increased research interest in controllable summarization that helps broaden access to technical texts and progress toward more usable abstractive summarization models in the biomedical domain. It is the second shared task being run as part of the BioNLP workshop 2023, which is being hosted at ACL 2023 in Toronto, Canada (July 13th or 14th).

For more information, see:

- Main site: https://biolaysumm.org/

- Subtask 1 - Lay Summarisation - CodaLab page: https://codalab.lisn.upsaclay.fr/competitions/9541

- Subtask 2 - Readability-controlled Summarization - CodaLab page: https://codalab.lisn.upsaclay.fr/competitions/9544

Detailed descriptions of the motivation, the tasks, and the data are also published in:

- Goldsack, T., Zhang, Z., Lin, C., Scarton, C.. Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature. EMNLP 2022.

- Luo, Z., Xie, Q., Ananiadou, S.. Readability Controllable Biomedical Document Summarization. EMNLP 2022 Findings.

*Organizers:*

- Chenghua Lin, Deputy Director of Research in the Department of Computer Science, University of Sheffield. - Sophia Ananiadou, Turing Fellow, Director of the National Centre for Text Mining, and Deputy Director of the Institute of Data Science and AI at the University of Manchester - Carolina Scarton, Department of Computer Science, University of Sheffield - Qianqian Xie, University of Manchester - Tomas Goldsack, University of Sheffield - Zheheng Luo, University of Manchester - Zhihao Zhang, Beihang University