  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.
