Generation of Non-redundant Summary based on Sentence Clustering Algorithms of NSGA-II and SPEA2

Abstract

In this paper, automatic document summarization using the sentence clustering algorithms, NSGA-II and SPEA2, is proposed. These algorithms are very effective to extract the most important and non-redundant sentences from a document. Using these, we cluster similar sentences as many groups as we need and extract the most important sentence in each group. After clustering, we rearrange the extracted sentences in the same order as in the document to generate readable summary. We tested this technique with two of the open benchmark datasets, DUC01 and DUC02. To evaluate the performances, we used F-measure and ROUGE. The experimental results show the performances of these MOGAs, NSGA-II and SPEA2, are better than those of the existing algorithms.