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
this https URL.

本文提出了一种新的任务：层次化重建多页文档的语义结构，并介绍了基于编码 - 解码结构的层次性文档结构分析系统 (DSPS) 来解决该问题。通过采用多模式双向编码器和结构感知 GRU 解码器，以及软屏蔽操作，DSPS 模型的表现优于基线方法。此外，作者还构建了一个大规模数据集 HRDoc，并公开发布。