cs.IR提交于 2026-11-02已译

以页面为中心的视觉文档检索定义与现有基准评测

Defining Page-Centric Visual Document Retrieval and Evaluating Existing Benchmarks

Chongzhi Zhang · Yuqi Zuo · Aixin Sun

中文摘要

现代信息检索（IR）中，文档越来越多地以视觉丰富、分页的结构化序列形式出现，而非扁平文本，例如 Word 文件、PDF 和幻灯片。以自由文本为中心的检索系统要么完全忽略这种结构，要么通过任意段落切分将其破坏。该论文将页面作为基本索引单元、并利用文档上下文评估页面相关性，形式化了以页面为中心的视觉文档检索（VDR）。该形式化区分了页面与文档两种检索粒度，以及独立条目、证据集和迭代检索三种检索范式。随后用八项标准评估了 42 个基准，标准覆盖语料特征、查询构建、相关性标注和排序支持。分析得出关于当前 VDR 研究的两个主要发现：第一，评估仍集中在页面级独立条目检索，支持文档级、证据集和迭代设置的资源较少；第二，独立于目标语料内容、源自真实用户信息需求的查询以及分级相关性判断并不常见，而相关性判断的覆盖范围和验证方式差异很大。两个发现都揭示了应用所需能力与现有评估协议所能确立的能力之间的差距。论文提供了基准选择指南，并指出未来基准设计的优先事项，强调任务需求、相关性语义与评估协议之间的一致性。

关键要点

01将页面作为基本索引单元，形式化以页面为中心的视觉文档检索（VDR），区分页面与文档两种粒度。
02提出独立条目、证据集和迭代检索三种范式，并给出八项标准评估框架。
03评估 42 个基准发现：现有资源集中于页面级独立条目检索，文档级与迭代设置支持不足。
04真实用户信息需求驱动的查询和分级相关性判断较少，相关性标注覆盖与验证差异大。
05指出现有能力与评估协议间的差距，提供基准选择指南并明确未来设计优先事项。

解读

尚无解读。

原始英文摘要

arXiv:2610.41360v1 Announce Type: cross Abstract: In modern information retrieval (IR), documents are increasingly encountered as structured sequences of visually rich, paginated units, rather than as flat text. Example documents encountered in our daily life include Word files, PDFs, and slide decks. Free text-centric retrieval systems either ignore this structure entirely or destroy it through arbitrary passage chunking. In this paper, we formulate page-centric Visual Document Retrieval (VDR) with pages as the fundamental indexing units and document context available for assessing page relevance. The formulation distinguishes two retrieval granularities, page and document, and three retrieval paradigms: independent-item, evidence-set, and iterative retrieval. We then assess 42 benchmarks using eight criteria covering corpus characteristics, query construction, relevance annotation, and ranking support. Our analysis shows two main findings on current VDR research. First, evaluation remains concentrated on page-level independent-item retrieval, with fewer resources supporting document-level, evidence-set, and iterative settings. Second, queries originating from real user information needs independently of target corpus content and graded relevance judgments are uncommon, while the coverage and verification of relevance judgments vary substantially. Both findings reveal gaps between the capabilities applications require and those existing evaluation protocols can establish. We provide benchmark selection guidance and identify priorities for future benchmark design, emphasizing alignment between task requirements, relevance semantics, and evaluation protocols.