Abstrakt

Document Summarization and Classification using Concept and Context Similarity Analysis

J.Arun, C. Gunavathi M.E

“Document summarization and classification using concept and context similarity analysis’’ deals with an information retrieval task, which aims at extracting a condensed version of the original document. A document summary is useful since it can give an overview of the original document in a shorter period of time. The main goal of a summary is to present the main ideas in a document/set of documents in a short and readable paragraph. Classification is a data mining function that assigns items in a collection to target categories of the documents. Context sensitive document indexing model based on the Bernoulli model of randomness is used for document summarization process. The lexical association between terms is used to produce a context sensitive weight to the document terms. The context sensitive indexing weights are used to compute the sentence similarity matrix and as a result, the sentences are presented in such a way that the most informative sentences appear on the top of the summary, making a positive impact on the quality of the summary