解决法律类书籍的深度章节结构解析难题，提升段落边界识别精度。
HiPS: Hierarchical PDF Segmentation of Doctrinal Legal Books
- 结合目录元数据与大模型优化的双通道解析流程
- 在9812个手工标注标题上实现高精度层次重建
- 适合法律文献数字化与司法文本分析研究者
PDF解析器在页面级版式理解方面已有进步，但对于深层结构化书籍，仍难以可靠恢复文档级的章节层级关系：许多系统仅能识别页内标题角色，假设层级较浅，或依赖高质量的PDF标签与目录元数据，且公开的深层书籍层级金标准数据稀缺。本文提出HiPS，用于教义性法律书籍的分层PDF分割，并做出两项主要贡献：首先，发布了一个包含49本开放获取法律书籍的金标准基准数据集，涵盖9,812个手工标注的标题、层级关系和页码锚点，支持标题检测、层级重构与章节边界判定的评估；其次，提出互补的分割流水线：一种基于目录元数据的解析器，适用于元数据完整的书籍；另一种无目录的LLM精炼流水线，融合OCR空白区域线索、XML排版信息与局部上下文。在广泛对比开源解析器及多模态/大模型基线时，有目录的流水线在元数据完整时表现最佳，而无目录的LLM精炼流水线在元数据缺失或噪声环境下显著提升标题精确率、深层结构恢复能力与边界质量。
原文摘要 · Abstract (English)
PDF parsers have recently improved on page-level layout understanding. However, recovering a document-global section hierarchy with reliable boundaries remains brittle for deeply structured books: many systems expose only page-local heading roles, assume shallow depth, or rely on high-quality PDF tags or Table of Contents (TOC) metadata, and public gold-standard data for deep book hierarchies is scarce. We present HiPS for hierarchical PDF segmentation of doctrinal legal books and make two main contributions. First, we release a gold-standard benchmark of 49 open-access law books with 9,812 manually curated headings, hierarchy levels, and page anchors, enabling evaluation of title detection, hierarchy reconstruction, and section boundary assignment. Second, we introduce complementary segmentation pipelines: a TOC-based parser for books with reliable outline metadata and a TOC-free LLM-refined pipeline that combines OCR whitespace cues, XML typography, and local context. Across a broad comparison against open-source parsers and multimodal/LLM baselines, the TOC-based pipeline is strongest when metadata is complete, while the LLM-refined pipeline improves heading precision, deep-level recovery, and boundary quality when metadata is missing or noisy.
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