Citation-Worthy Detection of URL Citations in Scholarly Papers
Kazuhiro Wada, Masaya Tsunokake, Shigeki Matsubara · 2024
Citations are crucial in scholarly papers, aiding in the acknowledgment of previous research and enhancing accessibility to related works. Creating accurate citations takes time and requires expertise. To promote appropriate citations, previous studies have tackled the citation-worthy detection of reference tags, which detect the sentence that needs citations. However, scholarly papers also use URL citations, which are pivotal yet understudied. This paper introduces the novel task of citation-worthy detection for URL citations. This task aims to detect the locations within sentences where URL citations are needed. In experiments, we compared a transfer learning method using Named Entity Recognition (NER) with a simple token classification approach. The NER-based method performed better on citations after the noun and demonstrated better learning ability despite distribution gaps between training and test sets. These findings indicate that leveraging scientific domain knowledge through NER is a promising approach for accurate URL citations detection.