docmaster-hierarchical-structure-aware-document-analysis

name	docmaster-hierarchical-structure-aware-document-analysis
description	Hierarchical structure-aware document analysis system using LLMs. Parses documents into hierarchical trees preserving layouts, builds structure-aware semantic indices for filtering and question answering. Handles academic papers, technical manuals, financial reports. Activation: document analysis, hierarchical structure, LLM system, information extraction, document understanding.
metadata	{"arxiv_id":"2607.08539","published":"2026-07-09","authors":"Ziqi Chen, Yingli Zhou, Fangyuan Zhang, Quanqing Xu, Chuanhui Yang","tags":["document-analysis","hierarchical-structure","llm-system","information-extraction","document-understanding"]}

DocMaster: A Hierarchical Structure-Aware System for Document Analysis

Overview

DocMaster is a hierarchical structure-aware document analysis system that preserves the rich structural information (sections, tables, figures, equations) in complex documents, unlike existing systems that flatten documents into plain-text chunks. It parses documents into hierarchical document trees and constructs structure-aware semantic indices for accurate document filtering and in-depth analysis.

Key Innovations

Hierarchical Document Trees

• Parses documents preserving original layouts (sections, tables, figures, equations)
• Maintains structural relationships lost in flat chunking
• Enables structure-aware retrieval and analysis

Structure-Aware Semantic Index

• Multi-view semantic indices built from hierarchical tree
• Enables accurate document filtering from large collections
• Supports natural-language filtering conditions

Integrated Workflow

• Document collection upload and indexing
• Natural-language filtering of relevant documents
• Follow-up question answering over filtered results
• Interactive web interface for end-user accessibility

Methodology

1. Document Parsing: Parse into hierarchical document trees preserving layout
2. Semantic Indexing: Build tree-based and multi-view semantic indices
3. Document Filtering: Filter relevant documents via natural-language conditions
4. Question Answering: Perform in-depth QA over filtered results
5. Interactive Interface: Web UI for upload, indexing, filtering, and QA

Implications

• Document structure preservation significantly improves LLM-based analysis
• Hierarchical trees enable more accurate retrieval than flat chunks
• Multi-view indices support diverse query types
• Interactive system makes advanced document analysis accessible

Pitfalls

• Parsing complex documents with mixed content (tables, equations) is error-prone
• Hierarchical tree construction adds preprocessing overhead
• Semantic index quality depends on embedding model quality
• Web interface may not scale to very large document collections

Activation Keywords

document analysis, hierarchical structure, DocMaster, LLM document system, semantic indexing, document filtering, question answering, information extraction

Paper Reference

arXiv:2607.08539 - "DocMaster: A Hierarchical Structure-Aware System for Document Analysis" (Jul 2026)