Improving Performance of Text Summarization

Today, the tremendous information is available on the internet; it is difficult to get the information fast and most efficiently. There are so many text materials available on the internet, in order to extract the most relevant information from it, we need a good mechanism. Text summarization technique deals with the compression of large document into shorter version of text. Text summarizations choose the most significant part of text and create coherent summaries that state the main purpose of the given document. Extraction based text summarization involves selecting sentences of high relevance (rank) from the document based on word and sentence features and put them together to generate summary. This is modeled using Fuzzy Inference System. The summary of the document is created based upon the level of the importance of the sentences in the document. This paper focuses on the Fuzzy logic Extraction approach for text summarization and the semantic approach of text summarization using Latent Semantic Analysis.