@inproceedings{sharma-etal-2023-team,
    title = "Team Cadence at {MEDIQA}-Chat 2023: Generating, augmenting and summarizing clinical dialogue with large language models",
    author = "Sharma, Ashwyn  and
      Feldman, David  and
      Jain, Aneesh",
    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-1.28",
    doi = "10.18653/v1/2023.clinicalnlp-1.28",
    pages = "228--235",
    abstract = "This paper describes Team Cadence{'}s winning submission to Task C of the MEDIQA-Chat 2023 shared tasks. We also present the set of methods, including a novel N-pass strategy to summarize a mix of clinical dialogue and an incomplete summarized note, used to complete Task A and Task B, ranking highly on the leaderboard amongst stable and reproducible code submissions. The shared tasks invited participants to summarize, classify and generate patient-doctor conversations. Considering the small volume of training data available, we took a data-augmentation-first approach to the three tasks by focusing on the dialogue generation task, i.e., Task C. It proved effective in improving our models{'} performance on Task A and Task B. We also found the BART architecture to be highly versatile, as it formed the base for all our submissions. Finally, based on the results shared by the organizers, we note that Team Cadence was the only team to submit stable and reproducible runs to all three tasks.",
}

This paper describes Team Cadence{'}s winning submission to Task C of the MEDIQA-Chat 2023 shared tasks. We also present the set of methods, including a novel N-pass strategy to summarize a mix of clinical dialogue and an incomplete summarized note, used to complete Task A and Task B, ranking highly on the leaderboard amongst stable and reproducible code submissions. The shared tasks invited participants to summarize, classify and generate patient-doctor conversations. Considering the small volume of training data available, we took a data-augmentation-first approach to the three tasks by focusing on the dialogue generation task, i.e., Task C. It proved effective in improving our models{'} performance on Task A and Task B. We also found the BART architecture to be highly versatile, as it formed the base for all our submissions. Finally, based on the results shared by the organizers, we note that Team Cadence was the only team to submit stable and reproducible runs to all three tasks.
Sharma, Ashwyn Feldman, David Jain, Aneesh
Team Cadence at MEDIQA-Chat 2023: Generating, augmenting and summarizing clinical dialogue with large language models