Data Summarization Using Maximal Marginal Relevance Method

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. Automatic text summarization is to compress an original document into an abridged version by extracting almost all of the essential concepts with text mining techniques. 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. Here, the approach for text summarization, in which three evidences are employed (clustering, binary tree and diversity based method) to help in finding the documents distinct ideas. The emphasis of the approach is on controlling the redundancy in the summarized text. The role of clustering is very important, therefore used K-means clustering algorithm. DocumentSentence Tree is build on which the MMI approach is applied to get the quality text Summary.