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SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation

  • Hassan H. Alrehamy
  • Coral WalkerEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)

Abstract

Keyphrases provide important semantic metadata for organizing and managing free-text documents. As data grow exponentially, there is a pressing demand for automatic and efficient keyphrase extraction methods. We introduce in this paper SemCluster, a clustering-based unsupervised keyphrase extraction method. By integrating an internal ontology (i.e., WordNet) with external knowledge sources, SemCluster identifies and extracts semantically important terms from a given document, clusters the terms, and, using the clustering results as heuristics, identifies the most representative phrases and singles them out as keyphrases. SemCluster is evaluated against two baseline unsupervised methods, TextRank and KeyCluster, over the Inspec dataset under an F1-measure metric. The evaluation results clearly show that SemCluster outperforms both methods.

Keywords

Keyphrase extraction Clustering-based AKE Unsupervised AKE 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsCardiff UniversityCardiffUK

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