A way to improve graph-based keyword extraction

Keyword extraction aims to find representative phrases for a document. Graph-based keyword extraction represent the input document as a graph and rank its nodes according to their score using graph-based ranking method. In this paper, we propose a method to compute importance of co-occurrence word in document and apply it in graph approach to find more representative phrases; introduce words correlation degree in document language network to improve performance when extracting average number of keyword in document. The experiment results show the effectiveness of proposed approach.