An unsupervised keyphrase extraction model by incorporating structural and semantic information

We proposed an unsupervised keyphrase extraction model that incorporates the structural information and the semantic information of a document. The structural information refers to the directed graph that is composed of keyphrase candidates and topics. The weight between two candidates is computed by their relative distance in the document and the positions of the corresponding sentences. Graph ranking algorithm is then applied to get the structural scores of the candidates. Then, the semantic score is obtained by the similarity between candidate and all sentences. The final score of a candidate is the sum of the structural score and the semantic score. The top N candidates with the highest scores are selected as the recommended keyphrases. The comparison experiments on three widely used datasets show that our model achieves the best results in the long documents and a competitive result in the short document. It indicates that our model is effective and is superior to the state-of-the-art unsupervised models.