  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.
