  Understanding and extracting of information from large documents, such as
business opportunities, academic articles, medical documents and technical
reports, poses challenges not present in short documents. Such large documents
may be multi-themed, complex, noisy and cover diverse topics. We describe a
framework that can analyze large documents and help people and computer systems
locate desired information in them. We aim to automatically identify and
classify different sections of documents and understand their purpose within
the document. A key contribution of our research is modeling and extracting the
logical and semantic structure of electronic documents using deep learning
techniques. We evaluate the effectiveness and robustness of our framework
through extensive experiments on two collections: more than one million
scholarly articles from arXiv and a collection of requests for proposal
documents from government sources.
