Automatic related work generation must ground their outputs to the content of
the cited papers to avoid non-factual hallucinations, but due to the length of
scientific documents, existing abstractive approaches have conditioned only on
the cited paper \textit{abstracts}. We demonstrate that the abstract is not
always the most appropriate input for citation generation and that models
trained in this way learn to hallucinate. We propose to condition instead on
the \textit{cited text span} (CTS) as an alternative to the abstract. Because
manual CTS annotation is extremely time- and labor-intensive, we experiment
with automatic, ROUGE-based labeling of candidate CTS sentences, achieving
sufficiently strong performance to substitute for expensive human annotations,
and we propose a human-in-the-loop, keyword-based CTS retrieval approach that
makes generating citation texts grounded in the full text of cited papers both
promising and practical.

基于引用文献的自动相关工作生成应当将输出结果与引用论文的内容相关联，以避免虚构产生了，然而由于科学文献的篇幅限制，现有的抽象方法只依赖于引用论文的摘要。我们证明了摘要并不总是产生引文时最适合的输入，这样训练出来的模型会出现虚构结果。相反，我们提出使用引用文本片段作为替代输入。由于手动标注引文文本片段非常耗时耗力，我们尝试了基于 ROUGE 的候选引文文本片段标注，并取得了足够强的性能，可以代替昂贵的人工标注，并提出了基于关键词的引文文本片段检索方法，使得生成以引用论文的全文为基础的引文文本成为可能也变得实际可行。

引文文本生成之引文部分识别

Cited Text Spans for Citation Text Generation

Academic research is an exploration activity to solve problems that have
never been resolved before. By this nature, each academic research work is
required to perform a literature review to distinguish its novelties that have
not been addressed by prior works. In natural language processing, this
literature review is usually conducted under the "Related Work" section. The
task of automatic related work generation aims to automatically generate the
"Related Work" section given the rest of the research paper and a list of cited
papers. Although this task was proposed over 10 years ago, it received little
attention until very recently, when it was cast as a variant of the scientific
multi-document summarization problem. However, even today, the problems of
automatic related work and citation text generation are not yet standardized.
In this survey, we conduct a meta-study to compare the existing literature on
related work generation from the perspectives of problem formulation, dataset
collection, methodological approach, performance evaluation, and future
prospects to provide the reader insight into the progress of the
state-of-the-art studies, as well as and how future studies can be conducted.
We also survey relevant fields of study that we suggest future work to consider
integrating.

此篇综述介绍了如何通过自然语言处理实现学术研究中 “Related Work” 部分的自动生成和如何将相关研究进行比较和未来展望。