Support Vector Machine (SVM) is

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Now a day when huge amount of documents and web contents are available, so reading of full content is somewhat difficult. Summarization is a way to give abstract form of large document so that the moral of the document can be communicated easily. Current research in automatic summarization is dominated by some effective, yet naive approaches: summarization through extraction, summarization through Abstraction and multi-document summarization. These techniques are used to building a summary of a document. Although there are a number of techniques implemented for the summarization of text for the single document or for the online web data or for any language. Here in this paper we are implemented an efficient technique for text summarization to reduce the computational cost and time and also the storage capacity.

Random Tree is a popular data classification classifier for machine learning. Feature reduction is one of the important research issues in big data. Most existing feature reduction algorithms are now faced with two challenging problems. On one hand, they have infrequently taken granular computing into thinking. On the other hand, they still cannot deal with massive data. Massive data processing is a difficult problem in the age of big data. Traditional feature reduction algorithms are generally time-consuming when facing big data. For speedily processing, we introduce a scalable fast approximate attribute reduction algorithm with Map Reduce. We divide the original data into many tiny chunks, and use reduction algorithm for each chunk. The reduction algorithm is based on correlation feature selection and generates decision rules by using Random Tree Classifier. Finally, feature reduction algorithm is proposed in data and task parallel using Hadoop Map Reduce framework with WEKA environment. Experimental results demonstrate that the proposed classifier can scale well and efficiently process big data.

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