Source note2 min read
DocMaster: A Hierarchical Structure-Aware System for Document Analysis
Source
arXiv
arxiv.org/abs/2607.08539v1
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 designed to analyze complex documents while preserving their hierarchical structures such as sections, tables, figures, and equations. It allows users to upload document collections, construct tree-based semantic indices for accurate filtering, perform question answering over filtered results, and offers an interactive web interface.
What the Document Says
- DocMaster is a system for analyzing complex documents with hierarchical structures.
- It enables users to upload document collections.
- The system constructs a structure-aware semantic index.
- This index allows for accurate document filtering and in-depth analysis (question answering).
- An interactive web interface is provided.
Key Concepts Mentioned
- DocMaster
- large language models
- academic papers
- technical manuals
- financial reports
- hierarchical structures
- semantic index
- question answering
- interactive web interface
Claims Made by the Document
- Existing systems flatten documents into plain-text chunks.
- Discarding hierarchical structures degrades downstream performance.
Evidence Used
- Users must first filter relevant documents from large collections and then conduct in-depth analysis over the selected subset, yet existing systems flatten documents...
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/.
