HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures

• URL: http://arxiv.org/abs/2303.13839v1
• Date: Fri, 24 Mar 2023 07:23:56 GMT
• Title: HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures
• Authors: Jiefeng Ma, Jun Du, Pengfei Hu, Zhenrong Zhang, Jianshu Zhang, Huihui Zhu, Cong Liu
• Abstract summary: This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. We built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. We propose an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem.
• Score: 31.868926876151342
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The problem of document structure reconstruction refers to converting digital or scanned documents into corresponding semantic structures. Most existing works mainly focus on splitting the boundary of each element in a single document page, neglecting the reconstruction of semantic structure in multi-page documents. This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. To better evaluate the system performance on the new task, we built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. Every document in HRDoc has line-level annotations including categories and relations obtained from rule-based extractors and human annotators. Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem. By adopting a multi-modal bidirectional encoder and a structure-aware GRU decoder with soft-mask operation, the DSPS model surpass the baseline method by a large margin. All scripts and datasets will be made publicly available at https://github.com/jfma-USTC/HRDoc.

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