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• W2131481773 abstract "One of the main purposes of a summary is to be able to replace the original document. We assume that a summary would be able to replace or act as a substitute for the document if its probability distribution is similar to that of the original document. With this hypothesis in mind, we carry out our analysis using datasets from Document Understanding Conference (DUC), studying the results of unigram probability models and also looking at Kullback-Leibler Divergence (KLD) as a measure of ‘closeness’ between probability models of the documents and their summaries. In this paper, we also discuss two summary generation approaches that were designed based on the above hypothesis (a) Summary generation by extraction of sentences based on its coverage and (b) Minimum KLD Summary Generation Method (uses a metric for reducing redundancy). Our research shows that the above summarizer, which is light and simple, can deliver good summaries comparable to other state-of-the-art systems." @default.
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• W2131481773 date "2009-12-01" @default.
• W2131481773 modified "2023-09-23" @default.
• W2131481773 title "Summarization Approaches Based on Document Probability Distributions" @default.
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