Enhancing keyphrase extraction from academic articles with their reference information

Chengzhi Zhang (), Lei Zhao, Mengyuan Zhao and Yingyi Zhang
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Chengzhi Zhang: Nanjing University of Science and Technology
Lei Zhao: Nanjing University of Science and Technology
Mengyuan Zhao: Nanjing University of Science and Technology
Yingyi Zhang: Nanjing University of Science and Technology

Scientometrics, 2022, vol. 127, issue 2, No 3, 703-731

Abstract: Abstract With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Naïve Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the research on automatic keyphrase extraction.

Keywords: Keyphrase extraction; References; Unsupervised learning; Supervised learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11192-021-04230-4

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