DocMaster保留文档层级结构，提升复杂文档的精准检索与问答能力。
DocMaster: A Hierarchical Structure-Aware System for Document Analysis
- 构建层次化文档树，保留章节、表格、公式等原始布局
- 通过结构感知索引实现自然语言条件下的精准文档过滤
- 支持交互式网页界面，适合科研与产业界文档分析场景
利用大语言模型（LLMs）分析复杂文档——如学术论文、技术手册和财务报告——已成为研究与工业中的主流且关键任务。实际应用中，用户需先从大规模文档集合中筛选相关文档，再对选定子集进行深度分析（如问答），但现有系统将文档扁平化为纯文本块，丢失了丰富的层级结构（如章节、表格、图表、公式），导致下游性能下降。我们提出DocMaster，一个面向文档分析的层次结构感知系统。DocMaster将文档解析为保持原始布局的层次化文档树，并构建结构感知语义索引，支持准确的文档筛选与深入分析。我们通过交互式网页界面展示了DocMaster：用户可上传文档集合，构建基于树结构与多视图的语义索引，使用自然语言条件过滤相关文档，并在筛选结果上执行后续问答。源代码、数据及演示已公开于https://doc-master.github.io/。
原文摘要 · Abstract (English)
Leveraging large language models (LLMs) to analyze complex documents -- such as academic papers, technical manuals, and financial reports -- has emerged as a mainstream and critical task in both research and industry. In practice, users must first filter relevant documents from large collections and then conduct in-depth analysis (e.g. question answering) over the selected subset, yet existing systems flatten documents into plain-text chunks, discarding the rich hierarchical structures (sections, tables, figures, equations) and degrading downstream performance. We present DocMaster, a hierarchical structure-aware document analysis system. DocMaster parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis. We demonstrate DocMaster through an interactive web interface that enables users to upload document collections, construct tree-based and multi-view semantic indices, filter relevant documents via natural-language conditions, and perform follow-up question answering over the filtered results. The source code, data, and demo are available at https://doc-master.github.io/.
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