Abstract
The paper deals with keyphrase extraction problem for single documents, e.g. scientific abstracts. Keyphrase extraction task is important and its results could be used in a variety of applications: data indexing, clustering and classification of documents, meta-information extraction, automatic ontologies creation etc. In the paper we discuss an approach to keyphrase extraction, itsтАЩ first step is building of candidate phrases which are then ranked and the best are selected as keyphrases. The paper is focused on the evaluation of weighting approaches to candidate phrases in the unsupervised ex-traction 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.
Highlights
The paper deals with the keyphrase extraction problem for single documents
When we filter one-word phrases and arbitrary select the number of keyphrases as in the gold standard the F-score = 0.38 which is better than state of the art results for Inspec, which use complex ranking techniques [9][12][14]
This result shows that methods that weight phrases using information about phrase length should work good on Inspec dataset
Summary
The paper deals with the keyphrase extraction problem for single documents. We focus on analysis of approaches to keyphrase selection from a set of candidates, built for a document [5,6,7,8]. We have shown that usage of some measure estimated by researchers as suitable, in reality leads to the situation where measured phrases are selected almost randomly and such measures could be considered equivalent for the annotation task. The novel feature which is proposed in the paper, is based on the exclusion of one-word phrases from candidates,that increases significantly the an-notation quality.
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