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.

翻译：现有长文档问答系统通常将文本视为平面序列或采用启发式分块，这忽略了自然引导人类理解的话语结构。我们提出了一种基于修辞结构理论（RST）的话语感知分层框架，用于长文档问答。该方法将话语树转化为句子级表示，并利用大语言模型增强的节点表示来桥接结构与语义信息。该框架包含三项关键创新：面向长文本的语言通用话语解析、基于大语言模型的话语关系节点增强，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，通过融入话语结构，该方法在多语种、多体裁场景下均持续优于现有方法。此外，所提框架在不同文档类型和语言环境中表现出强鲁棒性。