通过树结构引导，分块解析长文档的层级与顺序关系
DOSA: A Tree-Guided, Self-Regressive Framework for Long Document Structure Analysis
- 分块处理文档，融合视觉、文本与布局特征预测对象间关系
- 在多页文档上实现最高4 F1点、19 TEDS点的性能提升
- 适合需要理解复杂文档结构的场景，如合同、报告分析
在视觉丰富的文档中，信息不仅存在于表格、标题和文本块等单个页面对象中，还体现在它们之间的结构关系上，因此文档结构分析对信息检索和文档理解至关重要。然而，在具有长距离依赖和异构版式特征的多页文档中，准确推断这些关系仍具挑战性。为此，我们提出一种树引导的自回归框架——文档结构分析器（DOSA），用于推断页面对象间的关联并重建文档级语义树。DOSA以分块方式处理文档，为每个页面对象融合视觉、文本和布局特征，并预测层级与顺序关系。所预测的关系被用于逐步构建语义树，该树随后作为结构上下文指导后续块的推理。在五个基准上的实验表明，DOSA有效提升了性能，在最困难的多页层次结构基准DocHieNet上，F1得分最高提升4点，TEDS得分提升19点。
原文摘要 · Abstract (English)
In visually-rich documents, information is encoded not only in individual page objects such as tables, headers, and text blocks, but also in the structural relations among them, making document structure analysis fundamental to information retrieval and document understanding. However, accurately inferring such relations remains challenging in multi-page documents with long-range dependencies and heterogeneous layouts. To address this, we propose a tree-guided and self-regressive framework, termed DOcument Structure Analyzer (DOSA), for inferring relations among page objects and reconstructing document-level semantic trees. DOSA processes documents chunk-by-chunk, fusing visual, textual, and layout features for each page object and predicting hierarchical and ordering relations. The predicted relations are used to incrementally construct a semantic tree, which is then leveraged as structural context to guide inference on subsequent chunks. Experimental results on five benchmarks demonstrate the effectiveness of DOSA, with improvements of up to 4 F1 points and 19 TEDS points on DocHieNet, the most challenging multi-page hierarchy benchmark.
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