@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.",
}

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.
Jeong, Yongbin Han, Ju-Hyuck Chae, Kyung Min Cho, Yousang Seo, Hyunbin Lim, KyungTae Choi, Key-Sun Hahm, Younggyun
Teddysum at MEDIQA-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization