Extracting Summary from Documents Using K-Mean Clustering Algorithm

Abstract: Extracting summary from the documents is a difficult task for human beings. There fore to generate summary automatically has to facilitate several challenges; as the system automates it can only extract the required information from the original document. This reduces the work to compress the original document and extract only essential information with one of the text mining technique known as “diversity”. This diversity helps to find the multiple means in the document. Document sentence use one of scoring technique MMR (Maximum Marginal Relevance) to get the quality text summary. MMR approach depends on the document sentences, and tries to apply restriction on the document sentence to get the relevance important sentence score by MMR, known as generic summarization approach.The generic summarize approach is employed with one of the clustering method known as K-Mean clustering to find the summary of the document. This method helps to process the data set through certain number of clusters and find the prior in the data sets. This helps to find the similarity of each document and generate the summary of the document.

Keywords: MMR (Maximum Marginal Relevance), K-Mean Clustering, Generating Summary