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.

本文介绍了一个基于语言学的相关工作自动生成框架，包括引入一个标注数据集 CORWA 和训练一个基于此标注数据集的强基线模型，并建议了一种新颖的循环抽象与人工参与的相关工作生成框架。