A Noun-Centric Keyphrase Extraction Model: Graph-Based Approach

The graph-based approach has proven to be the most effective method of extracting keyphrases. Existing graph-based extraction methods do not include nouns as a component, resulting in keyphrases that are not noun-centric, leading to low-quality keyphrases. Also, the clustering approach employed in most of the keyphrase extraction has not yielded good results. This study proposed an improved model for extracting keyphrases that uses a graph-based model with noun phrase identifiers and effective clustering techniques. Relevant data was collected from selected documents in the English language. A graph-based model was formulated by integrating the textrank algorithm for node ranking, a noun phrase identifier for noun phrase scoring, an affinity propagation algorithm for selecting cluster groups, and k-means for clustering. The formulated model was implemented and evaluated by benchmarking it with an existing model using recall, f-measure, and precision as performance metrics. Final results showed that the developed model has a higher precision of 5.5%, a recall of 5.3%, and an f-measure score of 5.5% over the existing model. This implied that the noun-centric keyphrase extraction ensured high-quality keyphrase extraction.