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Topical keyphrase extraction is used to summarize large collections of textdocuments. However, traditional methods cannot properly reflect the intrinsicsemantics and relationships of keyphrases because they rely on a simpleterm-frequency-based process. Consequently, these methods are not effective inobtaining significant contextual knowledge. To resolve this, we propose atopical keyphrase extraction method based on a hierarchical semantic networkand multiple centrality network measures that together reflect the hierarchicalsemantics of keyphrases. We conduct experiments on real data to examine thepracticality of the proposed method and to compare its performance with that ofexisting topical keyphrase extraction methods. The results confirm that theproposed method outperforms state-of-the-art topical keyphrase extractionmethods in terms of the representativeness of the selected keyphrases for eachtopic. The proposed method can effectively reflect intrinsic keyphrasesemantics and interrelationships.

Topical Keyphrase Extraction with Hierarchical Semantic Networks