DocMaster: A Hierarchical Structure-Aware System for Document Analysis
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 docum...
| Authors | Ziqi Chen, Yingli Zhou, Fangyuan Zhang et al. |
|---|---|
| Primary category | cs.DB |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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