In recent year, data is emerging rapidly in each and every domain such as social media, news, education, etc. Due to data excessiveness, there is a need for an automatic text summarizer which will be having an ability to summarize the data. Since the research importance focusing on Natural Language Processing (NLP), text summarization can be used in several fields. Text summarization is a process of extracting data from a documents and generating summarized text of that documents. Thus presents an important data to the users in a relatively more concise form. The study of various extractive summarization of text is made and an essential text summarization method is proposed on the basis of Support-Vector-Machine (SVM). The proposed model tries to improve the quality as well as performances of the summary generated by the clustering technique by cascading it with Support-Vector-Machine (SVM). The documents are preprocessed to get the tokens that are obtained after tokenization, stop word removal, case folding and stemming. The various similarity measures are utilized in order to identify the similarity between the sentences of the document and then they are grouped in cluster on the basis of their term frequency and inverse document frequency (tf-idf) values of the words.

Text Summarization, Extractive Summarization, Natural language Processing (NLP), Clustering, Support-Vector-Machine (SVM), Advanced Encryption Standard (AES), Tokens.

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