DocMaster: A Hierarchical Structure-Aware System for Document Analysis

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Ziqi Chen, Yingli Zhou, Fangyuan Zhang, Quanqing Xu, Chuanhui Yang, Yixiang Fang
• Venue: arXiv: cs.DB
• Published: 2026-07-09
• Collected: 2026-07-10
• URL: http://arxiv.org/abs/2607.08539v1
• PDF: https://arxiv.org/pdf/2607.08539v1
• DOI: UNKNOWN

Plain-Language Brief

DocMaster is a system that allows for the analysis of complex documents while preserving their hierarchical structures such as sections, tables, figures, and equations. It offers an interactive web interface where users can upload document collections, filter relevant documents using natural-language conditions, and perform question answering over the filtered results.

What the Document Says

• DocMaster is a system for hierarchical structure-aware document analysis
• It parses documents into hierarchical document trees preserving original layouts
• Constructs a structure-aware semantic index for accurate document filtering and in-depth analysis
• Provides an interactive web interface for uploading document collections, constructing tree-based and multi-view semantic indices, filtering relevant documents, and performing question answering
• Source code, data, and demo are available at https://doc-master.github.io/

Key Concepts Mentioned

• DocMaster
• hierarchical structure-aware document analysis
• interactive web interface
• document filtering
• question answering

Claims Made by the Document

• Existing systems flatten documents into plain-text chunks, discarding the rich hierarchical structures and degrading downstream performance
• DocMaster improves downstream performance by preserving original layouts and constructing a structure-aware semantic index

Evidence Used

• The text mentions that existing systems flatten documents into plain-text chunks, implying that they do not preserve hierarchical structures
• The text states that DocMaster preserves original layouts and constructs a structure-aware semantic index, implying it improves downstream performance

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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/.