Abstract
Lay summarization is essential but challenging, as it simplifies scientific information for non-experts and keeps them updated with the latest scientific knowledge. In our participation in the Shared Task: Lay Summarization of Biomedical Research Articles @ BioNLP Workshop (Goldsack et al., 2024), ACL 2024, we conducted a comprehensive evaluation on abstractive summarization of biomedical literature using Large Language Models (LLMs) and assessed the performance using ten metrics across three categories: relevance, readability, and factuality, using eLife and PLOS datasets provided by the organizers. We developed a two-stage framework for lay summarization of biomedical scientific articles. In the first stage, we generated summaries using BART and PEGASUS LLMs by fine-tuning them on the given datasets. In the second stage, we combined the generated summaries and input them to BioBART, and then fine-tuned it on the same datasets. Our findings show that combining general and domain-specific LLMs enhances performance.- Anthology ID:
- 2024.bionlp-1.69
- 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:
- 762–768
- Language:
- URL:
- https://aclanthology.org/2024.bionlp-1.69
- DOI:
- Cite (ACL):
- Akanksha Karotia and Seba Susan. 2024. BioLay_AK_SS at BioLaySumm: Domain Adaptation by Two-Stage Fine-Tuning of Large Language Models used for Biomedical Lay Summary Generation. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 762–768, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- BioLay_AK_SS at BioLaySumm: Domain Adaptation by Two-Stage Fine-Tuning of Large Language Models used for Biomedical Lay Summary Generation (Karotia & Susan, BioNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.bionlp-1.69.pdf