SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical Summarization
This work addresses the challenge of summarizing medical conversations for healthcare professionals, but it is incremental as it applies an existing method (in-context learning) to a specific domain.
The authors tackled medical dialogue summarization by using in-context learning with GPT-4, achieving 3rd place in section-wise summarization and 4th place in full-note summarization in the MEDIQA 2023 Shared Task.
Medical dialogue summarization is challenging due to the unstructured nature of medical conversations, the use of medical terminology in gold summaries, and the need to identify key information across multiple symptom sets. We present a novel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA 2023 Shared Task. Our approach for section-wise summarization (Task A) is a two-stage process of selecting semantically similar dialogues and using the top-k similar dialogues as in-context examples for GPT-4. For full-note summarization (Task B), we use a similar solution with k=1. We achieved 3rd place in Task A (2nd among all teams), 4th place in Task B Division Wise Summarization (2nd among all teams), 15th place in Task A Section Header Classification (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. We compare GPT-4 performance with several finetuned baselines. We find that GPT-4 summaries are more abstractive and shorter. We make our code publicly available.