用知识图谱总结多篇医学文献，提升问答准确率
Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph
- 用命题构建局部知识图谱，整合多文档信息
- 分层摘要使小模型在医学问答上表现超越基线
- 适合需要精准医学问答的科研与临床场景
在问答任务中，检索增强生成（RAG）已显著提升多个领域的性能。然而，如何有效捕捉多文档间的关联关系，尤其是在生物医学任务中，仍是未解难题。本文提出一种新方法：从检索到的文档中提取命题，构建局部知识图谱，并通过分层摘要生成上下文信息，引导小型语言模型完成问答。该方法在多个生物医学问答基准测试中达到或优于RAG基线性能。我们还针对方法的每一步进行了专项评估，验证了其有效性。
原文摘要 · Abstract (English)
In Question Answering (QA), Retrieval Augmented Generation (RAG) has revolutionized performance in various domains. However, how to effectively capture multi-document relationships, particularly critical for biomedical tasks, remains an open question. In this work, we propose a novel method that utilizes propositional claims to construct a local knowledge graph from retrieved documents. Summaries are then derived via layerwise summarization from the knowledge graph to contextualize a small language model to perform QA. We achieved comparable or superior performance with our method over RAG baselines on several biomedical QA benchmarks. We also evaluated each individual step of our methodology over a targeted set of metrics, demonstrating its effectiveness.
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