Kannada text summarization using Latent Semantic Analysis

Text Summarization is a method of reducing the original text document into a short description. This short version retains the meaning and information content of the original text document. It is a difficult task for human beings to generate the summary for very large documents manually. The linguistic and statistical features of sentence can be used to find the importance of sentences. The Latent Semantic Analysis (LSA) captures automatically the semantic relationships between the sentences as a human being thinks. In this paper Singular Value Decomposition (SVD) is used to generate the summary. SVD finds the dimensions of the sentence vectors which are principal and mutually orthogonal. These properties guaranty the relevance to original text document and non-redundancy respectively in machine generated summary.

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