IS_SUM is a summarizer developed at

The search for interesting information in a huge data collection is a tough job frustrating the seekers for that information. The automatic text summarization has come to facilitate such searching process. The selection of distinct ideas “diversity ” from the original document can produce an appropriate summary. Incorporating of multiple means can help to find the diversity in the text. In this paper, we propose approach for text summarization, in which three evidences are employed (clustering, binary tree and diversity based method) to help in finding the document distinct ideas. The emphasis of our approach is on controlling the redundancy in the summarized text. The role of clustering is very important, where some clustering algorithms perform better than others. Therefore we conducted an experiment for comparing two clustering algorithms (K-means and complete linkage clustering algorithms) based on the performance of our method, the results shown that k-means performs better than complete linkage. In general, the experimental results shown that our method performs well for text summarization comparing with the benchmark methods used in this study.