Translating document renderings (e.g. PDFs, scans) into hierarchical structures is extensively demanded in the daily routines of many real-world applications, and is often a prerequisite step of many downstream NLP tasks. Earlier attempts focused on different but simpler tasks such as the detection of table or cell locations within documents; however, a holistic, principled approach to inferring the complete hierarchical structure in documents is missing. As a remedy, we developed "DocParser": an end-to-end system for parsing the complete document structure - including all text elements, figures, tables, and table cell structures. To the best of our knowledge, DocParser is the first system that derives the full hierarchical document compositions. Given the complexity of the task, annotating appropriate datasets is costly. Therefore, our second contribution is to provide a dataset for evaluating hierarchical document structure parsing. Our third contribution is to propose a scalable learning framework for settings where domain-specific data is scarce, which we address by a novel approach to weak supervision. Our computational experiments confirm the effectiveness of our proposed weak supervision: Compared to the baseline without weak supervision, it improves the mean average precision for detecting document entities by 37.1%. When classifying hierarchical relations between entity pairs, it improves the F1 score by 27.6%.

本文提出了DocParser，它是一种端到端的系统，可以解析包括所有文本元素、嵌套图形、表格和表格单元结构在内的完整文档结构，并提出了一种基于弱监督的可扩展学习框架以提高文档结构解析性能。研究表明，相比于没有弱监督的基线，这种方法可以提高39.1％的文档实体检测平均精度，并提高35.8％的分层关系分类F1得分。

DocParser: 文档呈现的分层结构解析