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Online information has increased tremendously in today's age of Internet. Asa result, the need has arose to extract relevant content from the plethora ofavailable information. Researchers are widely using automatic textsummarization techniques for extracting useful and relevant information fromvoluminous available information, it also enables users to obtain valuableknowledge in a limited period of time with minimal effort. The summary obtainedfrom the automatic text summarization often faces the issues of diversity andinformation coverage. Promising results are obtained for automatic textsummarization by the introduction of new techniques based on graph ranking ofsentences, clustering, and optimization. This research work proposes CoRank, atwo-stage sentence selection model involving clustering and then ranking ofsentences. The initial stage involves clustering of sentences using a novelclustering algorithm, and later selection of salient sentences using CoRankalgorithm. The approach aims to cover two objectives: maximum coverage anddiversity, which is achieved by the extraction of main topics and sub-topicsfrom the original text. The performance of the CoRank is validated on DUC2001and DUC 2002 data sets.

CoRank: A clustering cum graph ranking approach for extractive summarization