International Journal of Engineering and Technology

As the amount of textual Information increases, we experience a need for Automatic Text Summarizers. In Automatic summarization a text document or a larger corpus of multiple documents are reduced to a short set of words or paragraph that conveys the main meaning of the text. In this paper we proposed various features of Summary extraction. In the proposed approach during training phase, the feature vector is computed for a set of sentences using the feature extraction technique. After that, the feature vector and their corresponding feature scores are used to train the neural network optimally. Later in the testing phase, the input document is subjected to pre-processing and feature extraction techniques. Finally, by making use of sentence score, the most important sentences are extracted from the input document. The experimentation is performed with the DUC 2002 dataset. The features that are to be applied depending upon the size of the Document are also analyzed. The comparative results of the proposed approach and that of MS-Word are also presented here.