@inproceedings{jeong-etal-2023-teddysum,
title = "Teddysum at {MEDIQA}-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization",
author = "Jeong, Yongbin and
Han, Ju-Hyuck and
Chae, Kyung Min and
Cho, Yousang and
Seo, Hyunbin and
Lim, KyungTae and
Choi, Key-Sun and
Hahm, Younggyun",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.42",
doi = "10.18653/v1/2023.clinicalnlp-1.42",
pages = "394--402",
abstract = "In this paper, we introduce the design and various attempts for TaskB of MEDIQA-Chat 2023. The goal of TaskB in MEDIQA-Chat 2023 is to generate full clinical note from doctor-patient consultation dialogues. This task has several challenging issues, such as lack of training data, handling long dialogue inputs, and generating semi-structured clinical note which have section heads. To address these issues, we conducted various experiments and analyzed their results. We utilized the DialogLED model pre-trained on long dialogue data to handle long inputs, and we pre-trained on other dialogue datasets to address the lack of training data. We also attempted methods such as using prompts and contrastive learning for handling sections. This paper provides insights into clinical note generation through analyzing experimental methods and results, and it suggests future research directions.",
}
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<abstract>In this paper, we introduce the design and various attempts for TaskB of MEDIQA-Chat 2023. The goal of TaskB in MEDIQA-Chat 2023 is to generate full clinical note from doctor-patient consultation dialogues. This task has several challenging issues, such as lack of training data, handling long dialogue inputs, and generating semi-structured clinical note which have section heads. To address these issues, we conducted various experiments and analyzed their results. We utilized the DialogLED model pre-trained on long dialogue data to handle long inputs, and we pre-trained on other dialogue datasets to address the lack of training data. We also attempted methods such as using prompts and contrastive learning for handling sections. This paper provides insights into clinical note generation through analyzing experimental methods and results, and it suggests future research directions.</abstract>
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%0 Conference Proceedings
%T Teddysum at MEDIQA-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization
%A Jeong, Yongbin
%A Han, Ju-Hyuck
%A Chae, Kyung Min
%A Cho, Yousang
%A Seo, Hyunbin
%A Lim, KyungTae
%A Choi, Key-Sun
%A Hahm, Younggyun
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F jeong-etal-2023-teddysum
%X In this paper, we introduce the design and various attempts for TaskB of MEDIQA-Chat 2023. The goal of TaskB in MEDIQA-Chat 2023 is to generate full clinical note from doctor-patient consultation dialogues. This task has several challenging issues, such as lack of training data, handling long dialogue inputs, and generating semi-structured clinical note which have section heads. To address these issues, we conducted various experiments and analyzed their results. We utilized the DialogLED model pre-trained on long dialogue data to handle long inputs, and we pre-trained on other dialogue datasets to address the lack of training data. We also attempted methods such as using prompts and contrastive learning for handling sections. This paper provides insights into clinical note generation through analyzing experimental methods and results, and it suggests future research directions.
%R 10.18653/v1/2023.clinicalnlp-1.42
%U https://aclanthology.org/2023.clinicalnlp-1.42
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.42
%P 394-402
Markdown (Informal)
[Teddysum at MEDIQA-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization](https://aclanthology.org/2023.clinicalnlp-1.42) (Jeong et al., ClinicalNLP 2023)
ACL
- Yongbin Jeong, Ju-Hyuck Han, Kyung Min Chae, Yousang Cho, Hyunbin Seo, KyungTae Lim, Key-Sun Choi, and Younggyun Hahm. 2023. Teddysum at MEDIQA-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 394–402, Toronto, Canada. Association for Computational Linguistics.