Enhancing Keyphrase Extraction from Academic Articles with their Reference Information

TL;DR

This paper investigates how reference information in academic articles can enhance automatic keyphrase extraction, demonstrating that including references improves extraction accuracy across various methods.

Contribution

It introduces the use of reference information in keyphrase extraction and evaluates its effectiveness with multiple extraction algorithms, offering a new perspective for improving accuracy.

Findings

Reference information increases precision, recall, and F1 scores.

Including references enhances keyphrase extraction performance.

The approach provides a new direction for future research in automatic keyphrase extraction.

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 (Na\"ive 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 following research on automatic keyphrase extraction.