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Understanding large, structured documents like scholarly articles, requestsfor proposals or business reports is a complex and difficult task. It involvesdiscovering a document's overall purpose and subject(s), understanding thefunction and meaning of its sections and subsections, and extracting low levelentities and facts about them. In this research, we present a deep learningbased document ontology to capture the general purpose semantic structure anddomain specific semantic concepts from a large number of academic articles andbusiness documents. The ontology is able to describe different functional partsof a document, which can be used to enhance semantic indexing for a betterunderstanding by human beings and machines. We evaluate our models throughextensive experiments on datasets of scholarly articles from arXiv and Requestfor Proposal documents.

Understanding and representing the semantics of large structured documents