MatViX: Multimodal Information Extraction from Visually Rich Articles

• URL: http://arxiv.org/abs/2410.20494v1
• Date: Sun, 27 Oct 2024 16:13:58 GMT
• Title: MatViX: Multimodal Information Extraction from Visually Rich Articles
• Authors: Ghazal Khalighinejad, Sharon Scott, Ollie Liu, Kelly L. Anderson, Rickard Stureborg, Aman Tyagi, Bhuwan Dhingra,
• Abstract summary: In materials science, extracting structured information from research articles can accelerate the discovery of new materials. We introduce textscMatViX, a benchmark consisting of $324$ full-length research articles and $1,688$ complex structured files. These files are extracted from text, tables, and figures in full-length documents, providing a comprehensive challenge for MIE.
• Score: 6.349779979863784
• License:
• Abstract: Multimodal information extraction (MIE) is crucial for scientific literature, where valuable data is often spread across text, figures, and tables. In materials science, extracting structured information from research articles can accelerate the discovery of new materials. However, the multimodal nature and complex interconnections of scientific content present challenges for traditional text-based methods. We introduce \textsc{MatViX}, a benchmark consisting of $324$ full-length research articles and $1,688$ complex structured JSON files, carefully curated by domain experts. These JSON files are extracted from text, tables, and figures in full-length documents, providing a comprehensive challenge for MIE. We introduce an evaluation method to assess the accuracy of curve similarity and the alignment of hierarchical structures. Additionally, we benchmark vision-language models (VLMs) in a zero-shot manner, capable of processing long contexts and multimodal inputs, and show that using a specialized model (DePlot) can improve performance in extracting curves. Our results demonstrate significant room for improvement in current models. Our dataset and evaluation code are available\footnote{\url{https://matvix-bench.github.io/}}.

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