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While many NLP pipelines assume raw, clean texts, many texts we encounter inthe wild, including a vast majority of legal documents, are not so clean, withmany of them being visually structured documents (VSDs) such as PDFs.Conventional preprocessing tools for VSDs mainly focused on word segmentationand coarse layout analysis, whereas fine-grained logical structure analysis(such as identifying paragraph boundaries and their hierarchies) of VSDs isunderexplored. To that end, we proposed to formulate the task as prediction of"transition labels" between text fragments that maps the fragments to a tree,and developed a feature-based machine learning system that fuses visual,textual and semantic cues.Our system is easily customizable to different typesof VSDs and it significantly outperformed baselines in identifying differentstructures in VSDs. For example, our system obtained a paragraph boundarydetection F1 score of 0.953 which is significantly better than a popularPDF-to-text tool with an F1 score of 0.739.

Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser