用轻量模型实现医疗问答论坛的视角化摘要，兼顾效率与准确
Medifact at PerAnsSumm 2025: Leveraging Lightweight Models for Perspective-Specific Summarization of Clinical Q&A Forums
- 基于Snorkel-BART-SVM流水线，用弱监督提升零样本学习能力
- 提取相关句并用BART-CNN生成摘要，在100支队伍中排名12
- 适合医疗信息抽取与临床辅助决策系统研究者参考
PerAnsSumm 2025挑战聚焦于面向视角的医疗问答摘要（Agarwal等，2025）。本文提出一种少样本学习框架，采用Snorkel-BART-SVM流水线对开放式的医疗社区问答（CQA）进行分类与摘要。通过Snorkel进行弱监督训练的SVM模型增强了零样本学习能力。提取式分类识别出与视角相关的句子，并使用预训练的BART-CNN模型进行摘要生成。该方法在共享任务中位列100支参赛队伍中的第12名，展示了计算效率和上下文准确性。通过利用预训练摘要模型，本工作推动了医疗CQA研究的发展，并为临床决策支持系统提供助力。
原文摘要 · Abstract (English)
The PerAnsSumm 2025 challenge focuses on perspective-aware healthcare answer summarization (Agarwal et al., 2025). This work proposes a few-shot learning framework using a Snorkel-BART-SVM pipeline for classifying and summarizing open-ended healthcare community question-answering (CQA). An SVM model is trained with weak supervision via Snorkel, enhancing zero-shot learning. Extractive classification identifies perspective-relevant sentences, which are then summarized using a pretrained BART-CNN model. The approach achieved 12th place among 100 teams in the shared task, demonstrating computational efficiency and contextual accuracy. By leveraging pretrained summarization models, this work advances medical CQA research and contributes to clinical decision support systems.
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