  Current language understanding approaches focus on small documents, such as
newswire articles, blog posts, product reviews and discussion forum entries.
Understanding and extracting information from large documents like legal
briefs, proposals, technical manuals and research articles is still a
challenging task. We describe a framework that can analyze a large document and
help people to know where a particular information is in that document. We aim
to automatically identify and classify semantic sections of documents and
assign consistent and human-understandable labels to similar sections across
documents. A key contribution of our research is modeling the logical and
semantic structure of an electronic document. We apply machine learning
techniques, including deep learning, in our prototype system. We also make
available a dataset of information about a collection of scholarly articles
from the arXiv eprints collection that includes a wide range of metadata for
each article, including a table of contents, section labels, section
summarizations and more. We hope that this dataset will be a useful resource
for the machine learning and NLP communities in information retrieval,
content-based question answering and language modeling.
