DocMaster: A Hierarchical Structure-Aware System for Document Analysis

2025-01-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Expert, long

Summary

DocMaster is a hierarchical structure-aware document analysis system designed to overcome limitations of existing systems that flatten complex documents like academic papers, technical manuals, and financial reports. It addresses challenges by preserving rich hierarchical structures, including sections, tables, figures, and equations. DocMaster parses documents into hierarchical document trees and constructs a structure-aware semantic index using LLM-guided constrained clustering (PC-KMeans) and cross-section hyper-edges. The system employs a tri-modal retrieval strategy to accurately filter relevant documents based on natural-language conditions and supports follow-up question answering via retrieval-augmented generation (RAG). An interactive web interface demonstrates its capabilities, allowing users to upload document collections, explore tree-based and multi-view semantic indices, and compare filtering results.

Key takeaway

For Machine Learning Engineers developing document analysis systems, consider integrating hierarchical document structures and cross-section semantic indexing. Your current flat-chunking approaches likely degrade performance on complex documents. Adopting DocMaster's tri-modal retrieval and LLM-guided clustering can significantly improve filtering accuracy and RAG-based question answering, providing more precise context for LLMs. Explore its open-source implementation to enhance your system's ability to handle intricate document layouts.

Key insights

Preserving document hierarchy and cross-section semantics significantly improves LLM-based document analysis and retrieval.

Principles

• Document hierarchy is crucial for effective LLM analysis.
• Cross-section semantic links enhance retrieval accuracy.
• LLM-guided constraints improve clustering for indexing.

Method

DocMaster parses documents into hierarchical trees, builds a semantic index using Structural Entropy Correlation (SEC) and LLM-constrained PC-KMeans, and performs tri-modal retrieval for filtering and RAG-based Q&A.

In practice

• Upload document collections for tree-based indexing.
• Filter documents using natural-language queries.
• Perform RAG-based Q&A on filtered results.

Topics

• Document Analysis
• Large Language Models
• Hierarchical Document Structures
• Retrieval-Augmented Generation
• Semantic Indexing
• Constrained Clustering
• Tri-modal Retrieval

Code references

• langchain-ai/langchain
• jerryjliu/llama_index

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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• DocMaster: A Hierarchical Structure-Aware System for Document Analysis — DocMaster improves LLM-based document analysis by preserving hierarchical structure through tree-based parsing and semantic indexing.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.