Multi-Document Summarization Using Graph-Based Iterative Ranking Algorithms and Information Theoretical Distortion Measures

Text summarization is an important field in the area of natural language processing and text mining. This paper proposes an extraction-based model which uses graph-based and information theoretic concepts for multi-document summarization. Our method constructs a directed weighted graph from the original text by adding a vertex for each sentence, and compute a weighted edge between sentences which is based on distortion measures. In this paper we proposed a combination of these two models by representing the input as a graph, using distortion measures as the weight function and a ranking algorithm. Finally, a ranking algorithm is applied to identify the most important sentences to be included in the summary. By defining a proper distortion measure and ranking algorithm, this model gains promising results on the DUC2002 which is a well known real world data set. The results and ROUGE-1 scores of our model is fairly close to other successful models.