IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation
Yu-Hsuan Wu, Ying-Jia Lin, Hung-Yu Kao
BioNLP and BioNLP-ST 2023 Short Paper
TLDR:
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 sum
You can open the
#paper-BioNLP_124
channel in a separate window.
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.