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ICETTR-2013
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Paper Title : Multi-Document Text Summarization using Mutual Reinforcement and Relevance Propagation Models Added with Query and Features Profile
Author Name : Poonam P. Bari , Shanta Sondur
Abstract : Text summarization is the process of abridging the larger text into a shorter version preserving its information content and meaning. Using text summarizer user gets sense of the full-text, or able to know its information content without reading all sentences within the larger text. Text summarization reduces the text by removing less useful data which helps user to find the required information quickly without wasting time in reading the whole text. Lot of work has been done for automatic text summarization. In this approach the technique based on the multi-document summarization using mutual reinforcement and relevance propagation models is modified by adding features profile to it. This enables us to group the document set into several topic themes and then these are clustered according to the query. Further proposed work identify the salient sentences from each cluster by applying feature profile and then these are ranked according to their weights of importance. Finally, the mutual reinforcement between the ranking of sentence set and ranking of the cluster set is found out using reinforcement after relevance propagation (RARP) algorithm.
Keywords : Feature profile, relevance propagation, mutual reinforcement, query focused, multi-document summarization.
Cite this article : Poonam P. Bari , Shanta Sondur " Multi-Document Text Summarization using Mutual Reinforcement and Relevance Propagation Models Added with Query and Features Profile " ,ICETTR-2013 ,Page No : 59-63.