  Table of contents (ToC) extraction aims to extract headings of different
levels in documents to better understand the outline of the contents, which can
be widely used for document understanding and information retrieval. Existing
works often use hand-crafted features and predefined rule-based functions to
detect headings and resolve the hierarchical relationship between headings.
Both the benchmark and research based on deep learning are still limited.
Accordingly, in this paper, we first introduce a standard dataset, HierDoc,
including image samples from 650 documents of scientific papers with their
content labels. Then we propose a novel end-to-end model by using the
multimodal tree decoder (MTD) for ToC as a benchmark for HierDoc. The MTD model
is mainly composed of three parts, namely encoder, classifier, and decoder. The
encoder fuses the multimodality features of vision, text, and layout
information for each entity of the document. Then the classifier recognizes and
selects the heading entities. Next, to parse the hierarchical relationship
between the heading entities, a tree-structured decoder is designed. To
evaluate the performance, both the metric of tree-edit-distance similarity
(TEDS) and F1-Measure are adopted. Finally, our MTD approach achieves an
average TEDS of 87.2% and an average F1-Measure of 88.1% on the test set of
HierDoc. The code and dataset will be released at:
https://github.com/Pengfei-Hu/MTD.
