Improving keyphrase extraction by using document topic information

The objective of automatic keyphrase extraction is to generate keyphrases for large number of documents. A weakness of earlier keyphrase extraction algorithms is that occasionally they have lesser coherence among the extracted keyphrases. This paper examines the effect of injecting the domain information of the document to the ranking phase of automatic keyphrase extraction. The proposed method utilizes the statistical similarity of the domain between the document and the automatically extracted keyphrases as the criteria for ranking the keyphrases. The method is evaluated on baseline as well as advanced methods like KEA and resulted in a considerable amount of growth in accuracy. To demonstrate the feasibility of this approach, a naive implementation is also provided. The method has the potential to be widely applicable in all Keyphrase extraction algorithms.