Year: 2024
Type: article
Abstract: ABSTRACT Keywords facilitate rapid comprehension of academic papers for scholars, enhancing research efficiency. As some papers lack author‐assigned keywords, automated keyword extraction becomes crucial. Addressing the limited utilization of external paper information beyond titles and abstracts in prior studies, this research proposed leveraging the highlights section, which summarizes contributions and novelties, for unsupervised keyword extraction. We demonstrated performance improvements across three unsupervised keyword extraction models by integrating highlight information into abstracts. Further analysis indicates we can filter out irrelevant abstract sentences based on their semantic similarity to the highlights. Overall, this study pioneers the exploration of utilizing paper highlights for keyword extraction, simultaneously boosting unsupervised keyword extraction performance and providing insights for other abstract‐based text mining applications.
DOI: 10.1002/pra2.1213
Authors: Xiang Yi, Yan Xinyi, Chengzhi Zhang
Institution: Nanjing University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Award: 基于学术文献全文内容的细粒度算法实体抽取与评估研究
OpenAlex ID: W4403432833