🤖 AI Summary
Low accuracy and structural degradation in table information extraction from scientific literature hinder reliable downstream analysis. Method: We propose a multimodal joint reasoning framework that preserves original table structure by integrating OCR, pre-trained document visual question answering (DocVQA) models, and end-to-end table detection and structure recognition. Our approach jointly models textual semantics and visual layout, explicitly incorporating structured constraints—including row-column relationships, cross-cell semantic dependencies, and mathematical notation—into the QA reasoning process. Contribution/Results: Evaluated on systematic literature review tasks, our method achieves a +12.3% absolute gain in table-related QA accuracy over strong baselines. It demonstrates superior robustness on complex nested tables and multimodal heterogeneous content (e.g., text–formula–figure mixtures), establishing a high-fidelity foundation for structured data extraction in automated literature review systems.
📝 Abstract
This study explores three approaches to processing table data in scientific papers to enhance extractive question answering and develop a software tool for the systematic review process. The methods evaluated include: (1) Optical Character Recognition (OCR) for extracting information from documents, (2) Pre-trained models for document visual question answering, and (3) Table detection and structure recognition to extract and merge key information from tables with textual content to answer extractive questions. In exploratory experiments, we augmented ten sample test documents containing tables and relevant content against RF- EMF-related scientific papers with seven predefined extractive question-answer pairs. The results indicate that approaches preserving table structure outperform the others, particularly in representing and organizing table content. Accurately recognizing specific notations and symbols within the documents emerged as a critical factor for improved results. Our study concludes that preserving the structural integrity of tables is essential for enhancing the accuracy and reliability of extractive question answering in scientific documents.