Introduction
The use of large language models (LLMs) for analyzing complex documents such as academic papers, technical manuals, and financial reports has become a critical task in both research and industry.
Background Issues
Existing systems often flatten documents into plain-text chunks, discarding rich hierarchical structures (sections, tables, figures, equations), which degrades downstream performance.
To address this challenge, we present DocMaster, a hierarchical structure-aware document analysis system.
Design of DocMaster
DocMaster parses documents into hierarchical document trees that preserve the original layout. Key features include:
- Structure-Aware Semantic Index: By retaining hierarchical structures, DocMaster enables more accurate document filtering and in-depth analysis.
- Interactive User Interface: Users can upload document collections, construct tree-based and multi-view semantic indices, filter relevant documents via natural language conditions, and perform follow-up question answering on the filtered results.
Code and Demo
The source code, data, and demo are available at DocMaster Official Site.
Blogger's Review: DocMaster significantly enhances the accuracy of information retrieval and analysis by preserving structural information in documents. This innovation presents a new solution for document analysis, particularly suitable for handling complex and diverse document types.
Its interactive interface greatly improves user experience, making it a noteworthy development in the field.