@inproceedings{wu-etal-2023-ikm,
title = "{IKM}{\_}{L}ab at {B}io{L}ay{S}umm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation",
author = "Wu, Yu-Hsuan and
Lin, Ying-Jia and
Kao, Hung-Yu",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.64",
doi = "10.18653/v1/2023.bionlp-1.64",
pages = "602--610",
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.",
}
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%0 Conference Proceedings
%T IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation
%A Wu, Yu-Hsuan
%A Lin, Ying-Jia
%A Kao, Hung-Yu
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wu-etal-2023-ikm
%X 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.
%R 10.18653/v1/2023.bionlp-1.64
%U https://aclanthology.org/2023.bionlp-1.64
%U https://doi.org/10.18653/v1/2023.bionlp-1.64
%P 602-610
Markdown (Informal)
[IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation](https://aclanthology.org/2023.bionlp-1.64) (Wu et al., BioNLP 2023)
ACL