分步提取关键句再生成答案，提升医疗问答事实性。
ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality
- 先用提示词或相似度重排筛选临床文本关键句
- 最佳模型事实性得分0.44，排名第八且事实性第一
- 适合医疗问答与可解释性要求高的场景
本文针对ArchEHR-QA 2025共享任务，提出三种自动患者问答方法。构建了一个基于提示的端到端基线，以及两种两阶段方法：先从临床文本中提取关键句子（通过提示或相似度重排），再基于这些句子生成最终答案。结果表明，基于重排的两阶段系统表现最佳，凸显各子任务选择合适策略的重要性。最优模型整体得分0.44，在30支队伍中排名第8，同时在事实性维度排名第一。
原文摘要 · Abstract (English)
This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text, by prompt or similarity ranking, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.
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