From Surface to Semantics: Semantic Structure Parsing for Table-Centric Document Analysis

2025-08-13

AI Summary

Existing document analysis research predominantly addresses superficial table-centric tasks—such as table detection and layout parsing—while neglecting deep semantic modeling of table-context relationships, thereby hindering cross-paragraph reasoning and consistency analysis. To address this gap, we propose DOTABLER, the first end-to-end framework for joint semantic structure parsing of tables and their surrounding textual context. DOTABLER introduces a unified parsing pipeline integrating layout-aware representation learning, fine-grained semantic matching, and explicit context-table relational modeling. Leveraging a newly constructed PDF-based dataset and domain-adaptive fine-tuning, it enables table-centered document structure modeling and domain-specific retrieval. Evaluated on nearly 4,000 pages of real-world documents, DOTABLER achieves over 90% Precision and F1-score—substantially outperforming strong baselines including GPT-4o—and establishes new state-of-the-art performance in table-context semantic analysis.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Documents are core carriers of information and knowl-edge, with broad applications in finance, healthcare, and scientific research. Tables, as the main medium for structured data, encapsulate key information and are among the most critical document components. Existing studies largely focus on surface-level tasks such as layout analysis, table detection, and data extraction, lacking deep semantic parsing of tables and their contextual associations. This limits advanced tasks like cross-paragraph data interpretation and context-consistent analysis. To address this, we propose DOTABLER, a table-centric semantic document parsing framework designed to uncover deep semantic links between tables and their context. DOTABLER leverages a custom dataset and domain-specific fine-tuning of pre-trained models, integrating a complete parsing pipeline to identify context segments semantically tied to tables. Built on this semantic understanding, DOTABLER implements two core functionalities: table-centric document structure parsing and domain-specific table retrieval, delivering comprehensive table-anchored semantic analysis and precise extraction of semantically relevant tables. Evaluated on nearly 4,000 pages with over 1,000 tables from real-world PDFs, DOTABLER achieves over 90% Precision and F1 scores, demonstrating superior performance in table-context semantic analysis and deep document parsing compared to advanced models such as GPT-4o.

Problem

• Deep semantic parsing of tables and their contextual associations
• Advanced cross-paragraph data interpretation and context-consistent analysis
• Precise extraction of semantically relevant tables from documents

Innovation

• Semantic parsing framework for table-context links
• Domain-specific fine-tuning of pre-trained models
• Table-centric document structure and retrieval