IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation

Yu Hsuan Wu; Ying Jia Lin; Hung Yu Kao

IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation

Yu Hsuan Wu
, Ying Jia Lin
, Hung Yu Kao

National Cheng Kung University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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. Our code is available at https://github.com/IKMLab/ BioLaySumm.

Original language	English
Title of host publication	BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop
Editors	Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
Publisher	Association for Computational Linguistics (ACL)
Pages	602-610
Number of pages	9
ISBN (Electronic)	9781959429852
State	Published - 2023
Externally published	Yes
Event	22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023 - Toronto, Canada Duration: 13 07 2023 → …

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)	0736-587X

Conference

Conference	22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023
Country/Territory	Canada
City	Toronto
Period	13/07/23 → …

Bibliographical note

Publisher Copyright:
© 2023 Association for Computational Linguistics.

Cite this

Wu, Y. H., Lin, Y. J., & Kao, H. Y. (2023). IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation. In D. Demner-fushman, S. Ananiadou, & K. Cohen (Eds.), BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop (pp. 602-610). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL).

Wu, Yu Hsuan ; Lin, Ying Jia ; Kao, Hung Yu. / IKM_Lab at BioLaySumm Task 1 : Longformer-based Prompt Tuning for Biomedical Lay Summary Generation. BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop. editor / Dina Demner-fushman ; Sophia Ananiadou ; Kevin Cohen. Association for Computational Linguistics (ACL), 2023. pp. 602-610 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

@inproceedings{f027a625bec64424aabb7b5fb37107c3,

title = "IKM\_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation",

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. Our code is available at https://github.com/IKMLab/ BioLaySumm.",

author = "Wu, \{Yu Hsuan\} and Lin, \{Ying Jia\} and Kao, \{Hung Yu\}",

note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023 ; Conference date: 13-07-2023",

year = "2023",

language = "英语",

series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

pages = "602--610",

editor = "Dina Demner-fushman and Sophia Ananiadou and Kevin Cohen",

booktitle = "BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop",

address = "澳大利亚",

}

Wu, YH, Lin, YJ & Kao, HY 2023, IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation. in D Demner-fushman, S Ananiadou & K Cohen (eds), BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), pp. 602-610, 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023, Toronto, Canada, 13/07/23.

IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation. / Wu, Yu Hsuan; Lin, Ying Jia; Kao, Hung Yu.
BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop. ed. / Dina Demner-fushman; Sophia Ananiadou; Kevin Cohen. Association for Computational Linguistics (ACL), 2023. p. 602-610 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - IKM_Lab at BioLaySumm Task 1

T2 - 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023

AU - Wu, Yu Hsuan

AU - Lin, Ying Jia

AU - Kao, Hung Yu

PY - 2023

Y1 - 2023

N2 - 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. Our code is available at https://github.com/IKMLab/ BioLaySumm.

AB - 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. Our code is available at https://github.com/IKMLab/ BioLaySumm.

UR - https://www.scopus.com/pages/publications/85174550346

M3 - 会议稿件

AN - SCOPUS:85174550346

T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics

SP - 602

EP - 610

BT - BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop

A2 - Demner-fushman, Dina

A2 - Ananiadou, Sophia

A2 - Cohen, Kevin

PB - Association for Computational Linguistics (ACL)

Y2 - 13 July 2023

ER -

Wu YH, Lin YJ, Kao HY. IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation. In Demner-fushman D, Ananiadou S, Cohen K, editors, BioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2023. p. 602-610. (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation

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