# Table-Of-Contents generation on contemporary documents

arXiv: https://arxiv.org/abs/1911.08836 · HTML: https://mlsanity.com/paper/1911.08836/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Seg2Act: Global Context-aware Action Generation for Document Logical Structuring](https://mlsanity.com/paper/2410.06802/) (2024)
- [Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application](https://mlsanity.com/paper/2105.09297/) (2021)

## Abstract (arXiv)

The generation of precise and detailed Table-Of-Contents (TOC) from a document is a problem of major importance for document understanding and information extraction. Despite its importance, it is still a challenging task, especially for non-standardized documents with rich layout information such as commercial documents. In this paper, we present a new neural-based pipeline for TOC generation applicable to any searchable document. Unlike previous methods, we do not use semantic labeling nor assume the presence of parsable TOC pages in the document. Moreover, we analyze the influence of using external knowledge encoded as a template. We empirically show that this approach is only useful in a very low resource environment. Finally, we propose a new domain-specific data set that sheds some light on the difficulties of TOC generation in real-world documents. The proposed method shows better performance than the state-of-the-art on a public data set and on the newly released data set.