Abstract
This article presents our submission to the Bio- LaySumm 2024 shared task: Lay Summarization of Biomedical Research Articles. The objective of this task is to generate summaries that are simplified in a concise and less technical way, in order to facilitate comprehension by non-experts users. A pre-trained BioBART model was employed to fine-tune the articles from the two journals, thereby generating two models, one for each journal. The submission achieved the 12th best ranking in the task, attaining a meritorious first place in the Relevance ROUGE-1 metric.- Anthology ID:
- 2024.bionlp-1.71
- 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:
- 780–785
- Language:
- URL:
- https://aclanthology.org/2024.bionlp-1.71
- DOI:
- Cite (ACL):
- Adrian Gonzalez Sanchez and Paloma Martínez. 2024. HULAT-UC3M at BiolaySumm: Adaptation of BioBART and Longformer models to summarizing biomedical documents. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 780–785, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- HULAT-UC3M at BiolaySumm: Adaptation of BioBART and Longformer models to summarizing biomedical documents (Gonzalez Sanchez & Martínez, BioNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.bionlp-1.71.pdf