  Academic research is an exploratory activity to discover new solutions to
problems. By this nature, academic research works perform literature reviews to
distinguish their novelties from prior work. In natural language processing,
this literature review is usually conducted under the "Related Work" section.
The task of related work generation aims to automatically generate the related
work section given the rest of the research paper and a list of papers to cite.
Prior work on this task has focused on the sentence as the basic unit of
generation, neglecting the fact that related work sections consist of variable
length text fragments derived from different information sources. As a first
step toward a linguistically-motivated related work generation framework, we
present a Citation Oriented Related Work Annotation (CORWA) dataset that labels
different types of citation text fragments from different information sources.
We train a strong baseline model that automatically tags the CORWA labels on
massive unlabeled related work section texts. We further suggest a novel
framework for human-in-the-loop, iterative, abstractive related work
generation.
