  Translating renderings (e. g. PDFs, scans) into hierarchical document
structures is extensively demanded in the daily routines of many real-world
applications. However, a holistic, principled approach to inferring the
complete hierarchical structure of documents is missing. As a remedy, we
developed "DocParser": an end-to-end system for parsing the complete document
structure - including all text elements, nested figures, tables, and table cell
structures. 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 are scarce,
which we address by a novel approach to weak supervision that significantly
improves the document structure parsing performance. Our 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 39.1 % and improves the F1 score of classifying
hierarchical relations by 35.8 %.
