Text summarization using Sentence-Level Semantic Graph Model

Text summarization is to generate a condensed version of the original document. The major issues for text summarization are eliminating redundant information, identifying important difference among documents and covering the informative content. In this paper, we propose a Sentence-Level Semantic Graph Model (SLSGM) which exploits the semantic information of sentence. SLSGM considers sentences as vertexes while the semantic relationship between sentences as the edges. We calculate the relevance values between sentences using semantic analysis and take the values as weights of edges while sentences' values are scored by a variant of the traditional PageRank graph ranking algorithm considering both internal and external information. The experimental results show that the applicability of the model to text summarization is feasible and effective.