Towards Knowledge-Guided Biomedical Lay Summarization using Large Language Models

Shufan Ming, Yue Guo, Halil Kilicoglu


Abstract
The massive size, continual growth, and technical jargon in biomedical publications make it difficult for laypeople to stay informed about the latest scientific advances, motivating research on lay summarization of biomedical literature. Large language models (LLMs) are increasingly used for this task. Unlike typical automatic summarization, lay summarization requires incorporating background knowledge not found in a paper and explanations of technical jargon. This study explores the use of MeSH terms (Medical Subject Headings), which represent an article’s main topics, to enhance background information generation in biomedical lay summarization. Furthermore, we introduced a multi-turn dialogue approach that more effectively leverages MeSH terms in the instruction-tuning of LLMs to enhance the quality of lay summaries. The best model improved the state-of-the-art on the eLife test set in terms of the ROUGE-1 score by nearly 2%, with competitive scores in other metrics. These results indicate that MeSH terms can guide LLMs to generate more relevant background information for laypeople. Additionally, evaluation on a held-out dataset, one that was not used during model pre-training, shows that this capability generalizes well to unseen data, further demonstrating the effectiveness of our approach.
Anthology ID:
2025.cl4health-1.24
Volume:
Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)
Month:
May
Year:
2025
Address:
Albuquerque, New Mexico
Editors:
Sophia Ananiadou, Dina Demner-Fushman, Deepak Gupta, Paul Thompson
Venues:
CL4Health | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
285–297
Language:
URL:
https://preview.aclanthology.org/fix-sig-urls/2025.cl4health-1.24/
DOI:
Bibkey:
Cite (ACL):
Shufan Ming, Yue Guo, and Halil Kilicoglu. 2025. Towards Knowledge-Guided Biomedical Lay Summarization using Large Language Models. In Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health), pages 285–297, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):
Towards Knowledge-Guided Biomedical Lay Summarization using Large Language Models (Ming et al., CL4Health 2025)
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PDF:
https://preview.aclanthology.org/fix-sig-urls/2025.cl4health-1.24.pdf