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HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures
Ma, Jiefeng, Du, Jun, Hu, Pengfei, Zhang, Zhenrong, Zhang, Jianshu, Zhu, Huihui, Liu, Cong
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 (a) Multi-page documents (b) Line-level classification 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.
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Text Processing (0.68)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Rule-Based Reasoning (0.67)
- Information Technology > Artificial Intelligence > Natural Language > Grammars & Parsing (0.55)
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