Ranking in keyphrase extraction problem: is it suitable to use statistics of words occurrences?

Abstract

The paper deals with keyphrase extraction problem for single docu-ments, e.g. scientific abstracts. Keyphrase extraction task is important and its re-sults could be used in a variety of applications: data indexing, clustering and clas-sification of documents, meta-information extraction, automatic ontologies crea-tion etc. In the paper we discuss an approach to keyphrase extraction, its' first step is building of candidate phrases which are then ranked and the best are se-lected as keyphrases. The paper is focused on the evaluation of weighting ap-proaches to candidate phrases in the unsupervised extraction methods. A number of in-phrase word weighting procedures is evaluated. Unsuitable approaches to weighting are identified. Testing of some approaches shows their equivalence as applied to keyphrase extraction. A feature, which allows to increase the quality of extracted keyphrases and shows better results in comparison to the state of the art, is proposed. Experiments are based on Inspec dataset.