Topical Keyphrase Extraction with Hierarchical Semantic Networks
This addresses the need for more effective summarization of text collections by improving keyphrase extraction, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of topical keyphrase extraction by proposing a method based on hierarchical semantic networks and centrality measures to better reflect keyphrase semantics and relationships, and it reports that the method outperforms state-of-the-art methods in representativeness.
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.