NewAgeHealthWarriors at MEDIQA-Chat 2023 Task A: Summarizing Short Medical Conversation with Transformers

EasyChair Preprint no. 10534

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.

Keyphrases: Clinical Note Generation, ClinicalNLP, Conversation Summarization, deep learning, dialogue summarization, GPT-3, Healthcare, machine learning, Natural Language Processing

@Booklet{EasyChair:10534,
  author = {Prakhar Mishra and Ravi Theja Desetty},
  title = {NewAgeHealthWarriors at MEDIQA-Chat 2023 Task A: Summarizing Short Medical Conversation with Transformers},
  howpublished = {EasyChair Preprint no. 10534},

  year = {EasyChair, 2023}}