Feature based cluster ranking approach for single document summarization

Text Summarization is a process of creating gist of large set of documents. It creates a summary which depicts the overall information contained in large text documents in a short and accurate way. A model for generating single document text summarization is presented in this paper. This model is based on extractive summarization. The proposed work extracts the informative features and generates the scoring of sentences by using similarity measure technique. Once the score of sentences is generated then clusters of sentences are formed. Clusters and sentences in each cluster are ranked and highly ranked sentences from each cluster of relative importance are included in the final summary. Summary of text document is created by identifying the important sentences from the document.