Topical Keyphrase Extraction with Hierarchical Semantic Networks

TL;DR

This paper introduces a hierarchical semantic network approach for topical keyphrase extraction, significantly improving the representation of keyphrase semantics and relationships over traditional frequency-based methods.

Contribution

It proposes a novel hierarchical semantic network model combined with centrality measures to better capture keyphrase semantics and interrelationships.

Findings

Outperforms existing methods in keyphrase representativeness

Effectively captures intrinsic keyphrase semantics

Reflects hierarchical relationships among keyphrases

Abstract

Topical keyphrase extraction is used to summarize large collections of text documents. However, traditional methods cannot properly reflect the intrinsic semantics and relationships of keyphrases because they rely on a simple term-frequency-based process. Consequently, these methods are not effective in obtaining significant contextual knowledge. To resolve this, we propose a topical keyphrase extraction method based on a hierarchical semantic network and multiple centrality network measures that together reflect the hierarchical semantics of keyphrases. We conduct experiments on real data to examine the practicality of the proposed method and to compare its performance with that of existing topical keyphrase extraction methods. The results confirm that the proposed method outperforms state-of-the-art topical keyphrase extraction methods in terms of the representativeness of the selected keyphrases for each topic. The proposed method can effectively reflect intrinsic keyphrase semantics and interrelationships.