CORWA: A Citation-Oriented Related Work Annotation Dataset

Jessica Ouyang, Xiangci Li, Biswadip Mandal

Abstract:

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.