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[Submitted on 9 Jul 2026]
Abstract: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 this https URL.
Submission history
From: Ziqi Chen [view email]
[v1]
Thu, 9 Jul 2026 14:33:47 UTC (739 KB)
추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2607.08539
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