@inproceedings{mishra-desetty-2023-newagehealthwarriors,
    title = "{N}ew{A}ge{H}ealth{W}arriors at {MEDIQA}-Chat 2023 Task A: Summarizing Short Medical Conversation with Transformers",
    author = "Mishra, Prakhar  and
      Desetty, Ravi Theja",
    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-1.44",
    doi = "10.18653/v1/2023.clinicalnlp-1.44",
    pages = "414--421",
    abstract = "This paper presents the MEDIQA-Chat 2023 shared task organized at the ACL-Clinical NLP workshop. The shared task is motivated by the need to develop methods to automatically generate clinical notes from doctor-patient conversations. In this paper, we present our submission for \textit{MEDIQA-Chat 2023 Task A: Short Dialogue2Note Summarization}. Manual creation of these clinical notes requires extensive human efforts, thus making it a time-consuming and expensive process. To address this, we propose an ensemble-based method over GPT-3, BART, BERT variants, and Rule-based systems to automatically generate clinical notes from these conversations. The proposed system achieves a score of 0.730 and 0.544 for both the sub-tasks on the test set (ranking 8th on the leaderboard for both tasks) and shows better performance compared to a baseline system using BART variants.",
}

This paper presents the MEDIQA-Chat 2023 shared task organized at the ACL-Clinical NLP workshop. The shared task is motivated by the need to develop methods to automatically generate clinical notes from doctor-patient conversations. In this paper, we present our submission for \textit{MEDIQA-Chat 2023 Task A: Short Dialogue2Note Summarization}. Manual creation of these clinical notes requires extensive human efforts, thus making it a time-consuming and expensive process. To address this, we propose an ensemble-based method over GPT-3, BART, BERT variants, and Rule-based systems to automatically generate clinical notes from these conversations. The proposed system achieves a score of 0.730 and 0.544 for both the sub-tasks on the test set (ranking 8th on the leaderboard for both tasks) and shows better performance compared to a baseline system using BART variants.
Mishra, Prakhar Desetty, Ravi Theja
NewAgeHealthWarriors at MEDIQA-Chat 2023 Task A: Summarizing Short Medical Conversation with Transformers