TableofContents Generation on Contemporary Documents The generation of precise and detailed TableOfContents (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 nonstandardized documents with rich layout information such as commercial documents. In this paper, we present a new neuralbased 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 domainspecific data set that sheds some light on the difficulties of TOC generation in realworld documents. The proposed method shows better performance than the stateoftheart on a public data set and on the newly released data set.

Attend, Copy, Parse Endtoend Information Extraction from Documents Document information extraction tasks performed by humans create data consisting of a PDF or document image input, and extracted string outputs. This endtoend data is naturally consumed and produced when performing the task because it is valuable in and of itself. It is naturally available, at no additional cost. Unfortunately, stateoftheart word classification methods for information extraction cannot use this data, instead requiring wordlevel labels which are expensive to create and consequently not available for many real life tasks. In this paper we propose the Attend, Copy, Parse architecture, a deep neural network model that can be trained directly on endtoend data, bypassing the need for wordlevel labels. We evaluate the proposed architecture on a large diverse set of invoices, and outperform a stateoftheart production system based on word classification. We believe our proposed architecture can be used on many real life information extraction tasks where word classification cannot be used due to a lack of the required wordlevel labels.