LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents

[2608.15064] LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.15064 (cs) [Submitted on 15 Aug 2026] Title: LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents Authors: Yuefeng Zou , Yichen Lu , Jingxiao Yang , Bingtao Fu , Gaoyang Zhang , Xiongfei Bai , Tian Chen , Xiang Qi View a PDF of the paper titled LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents, by Yuefeng Zou and 7 other authors View PDF HTML (experimental) Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence. Existing benchmarks focus on page-level element recognition, reading order, formula recognition, and table structure. Long documents, however, also require document-level structure recovery. This includes reconstructing cross-page table-of-contents (TOC) hierarchies and identifying typed links from tables and figures to their captions, notes, and sources, often in one-to-many form. Because these structures are covered only partially or subsumed within broader parsing protocols, existing benchmarks cannot directly evaluate two key document-level tasks: \emph{Table-of-Contents Hierarchy Recovery} and \emph{Contextual Relationship Recovery}. To benchmark these two tasks, we introduce \textsc{LongDocBench}, comprising 85 real-world financial reports, textbooks, and academic papers spanning 2,582 pages, with up to 105 pages per document. It provides human-verified annotations for 3,937 heading nodes (mean node depth 3.55; maximum depth 9) and 3,258 contextual relationships annotated across 2,680 table and figure objects. We further evaluate both the downstream utility and recoverability of these structures. Long-document question-answering experiments show that human-verified TOC hierarchies and contextual relationships improve reasoning, with their combination providing complementary benefits. Meanwhile, representative document parsers remain limited on both recovery tasks despite strong page-level performance. To support further progress, we publicly release \textsc{LongDocBench} and its evaluation protocol and reproducible testbed for advancing document-level structure recovery in long documents. Comments: preprint, under review Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15064 [cs.AI] (or arXiv:2608.15064v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15064 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jingxiao Yang [ view email ] [v1] Sat, 15 Aug 2026 06:25:26 UTC (7,983 KB) Full-text links: Access Paper: View a PDF of the paper titled LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents, by Yuefeng Zou and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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