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：现有的长文档问答系统通常将文本视为扁平序列或采用启发式分块处理，忽略了自然引导人类理解的篇章结构。本文提出一种基于修辞结构理论（RST）的篇章感知分层框架用于长文档问答。该方法将篇章树转化为句子级表征，并采用LLM增强的节点表征来桥接结构与语义信息。该框架包含三项核心创新：面向长文档的语言普适篇章解析、基于LLM的篇章关系节点增强，以及结构引导的分层检索机制。在四个数据集上的大量实验表明，通过融入篇章结构，本方法在多种文体和语言场景中均较现有方法取得持续改进。此外，所提框架在不同文档类型与语言环境下均展现出强鲁棒性。