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Medical dialogue summarization is challenging due to the unstructured natureof medical conversations, the use of medical terminology in gold summaries, andthe need to identify key information across multiple symptom sets. We present anovel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA2023 Shared Task. Our approach for section-wise summarization (Task A) is atwo-stage process of selecting semantically similar dialogues and using thetop-k similar dialogues as in-context examples for GPT-4. For full-notesummarization (Task B), we use a similar solution with k=1. We achieved 3rdplace in Task A (2nd among all teams), 4th place in Task B Division WiseSummarization (2nd among all teams), 15th place in Task A Section HeaderClassification (9th among all teams), and 8th place among all teams in Task B.Our results highlight the effectiveness of few-shot prompting for this task,though we also identify several weaknesses of prompting-based approaches. Wecompare GPT-4 performance with several finetuned baselines. We find that GPT-4summaries are more abstractive and shorter. We make our code publiclyavailable.

SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical Summarization