Text Summarization using Latent Semantic Analysis
Keywords:
Information retrieval, Latent Semantic Analysis (LSA), Singular Value Decomposition (SVD), text summarization componentAbstract
Text Summarization is an approach of creating compressed version of a given document using natural language processing that can help users to quickly understand the main points of a document. Text summarization aims at getting the most suggestive content in a system of condensed form from an enabled input document while it retains the semantic information of the text to a large extent. It is considered to be an effective way of handling the information which is found to be overloaded. It solves the problem of presenting information in more compact form. There are various approaches to produce well defined summaries and one of the newest methods is Latent Semantic Analysis (LSA). Though the available information about any topic is huge and unimaginable in numbers in recent years. So, there is a need for quick view of those articles to decide relatedness of the article to the user’s need. In this paper, the efficient way of summarizing the text document by involving the combination of the techniques and then evaluating it with the rouge scores was implemented. The Singular value decomposition plays major role in extracting important sentences from the input document. Every sentence is assigned with rank based on its importance in the original document. Sentence selection is done according to the ranks and the summary is generated. The rouge will produce three scores namely, Recall, Precision and the F-score. F-score is considered for evaluating the correctness of a summary. The comparison of three different summaries by reducing input document as 1/2nd, 1/3rd, 1/4th rouge scores and f-score are found to be obtain the effective results towards summarizing the text document.
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