用论述结构提升长文档问答，让模型理解文章逻辑层次。
Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering
- 基于修辞结构理论构建分层检索框架，解析文本论述关系。
- 在四个数据集上均显著优于现有方法，跨语言跨文体表现稳定。
- 适合需要理解复杂长文逻辑的场景，如法律、学术文献问答。
现有长文档问答系统通常将文本处理为扁平序列或采用启发式分块，忽略了自然引导人类理解的论述结构。本文提出一种考虑论述结构的分层框架，利用修辞结构理论（RST）进行长文档问答。该方法将论述树转换为句子级表示，并通过大语言模型增强节点表示，弥合结构与语义信息的差距。框架包含三大创新：适用于长文档的语言无关论述解析、基于大语言模型的论述关系节点增强、以及结构引导的分层检索。在四个数据集上的大量实验表明，引入论述结构后性能持续优于现有方法，覆盖多种文体和语言。此外，该框架在不同文档类型和语言环境下表现出强鲁棒性。
原文摘要 · Abstract (English)
Existing long-document question answering systems typically process texts as flat sequences or use heuristic chunking, which overlook the discourse structures that naturally guide human comprehension. We present a discourse-aware hierarchical framework that leverages rhetorical structure theory (RST) for long document question answering. Our approach converts discourse trees into sentence-level representations and employs LLM-enhanced node representations to bridge structural and semantic information. The framework involves three key innovations: language-universal discourse parsing for lengthy documents, LLM-based enhancement of discourse relation nodes, and structure-guided hierarchical retrieval. Extensive experiments on four datasets demonstrate consistent improvements over existing approaches through the incorporation of discourse structure, across multiple genres and languages. Moreover, the proposed framework exhibits strong robustness across diverse document types and linguistic settings.
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