Abstract
This paper describes the entry by the Intelligent Knowledge Management (IKM) Laboratory in the BioLaySumm 2023 task1. We aim to transform lengthy biomedical articles into concise, reader-friendly summaries that can be easily comprehended by the general public. We utilized a long-text abstractive summarization longformer model and experimented with several prompt methods for this task. Our entry placed 10th overall, but we were particularly proud to achieve a 3rd place score in the readability evaluation metric.- Anthology ID:
- 2023.bionlp-1.64
- Volume:
- The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 602–610
- Language:
- URL:
- https://aclanthology.org/2023.bionlp-1.64
- DOI:
- 10.18653/v1/2023.bionlp-1.64
- Cite (ACL):
- Yu-Hsuan Wu, Ying-Jia Lin, and Hung-Yu Kao. 2023. IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 602–610, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation (Wu et al., BioNLP 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.bionlp-1.64.pdf