UIUC_BioNLP at BioLaySumm: An Extract-then-Summarize Approach Augmented with Wikipedia Knowledge for Biomedical Lay Summarization

Zhiwen You, Shruthan Radhakrishna, Shufan Ming, Halil Kilicoglu

Abstract
As the number of scientific publications is growing at a rapid pace, it is difficult for laypeople to keep track of and understand the latest scientific advances, especially in the biomedical domain. While the summarization of scientific publications has been widely studied, research on summarization targeting laypeople has remained scarce. In this study, considering the lengthy input of biomedical articles, we have developed a lay summarization system through an extract-then-summarize framework with large language models (LLMs) to summarize biomedical articles for laypeople. Using a fine-tuned GPT-3.5 model, our approach achieves the highest overall ranking and demonstrates the best relevance performance in the BioLaySumm 2024 shared task.
Anthology ID:
2024.bionlp-1.11
Volume:
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
132–143
Language:
URL:
https://aclanthology.org/2024.bionlp-1.11
DOI:
Bibkey:
Cite (ACL):
Zhiwen You, Shruthan Radhakrishna, Shufan Ming, and Halil Kilicoglu. 2024. UIUC_BioNLP at BioLaySumm: An Extract-then-Summarize Approach Augmented with Wikipedia Knowledge for Biomedical Lay Summarization. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 132–143, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
UIUC_BioNLP at BioLaySumm: An Extract-then-Summarize Approach Augmented with Wikipedia Knowledge for Biomedical Lay Summarization (You et al., BioNLP-WS 2024)
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