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The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents.(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t This paper proposes an optimization-based model for generic document(More)
In this paper is proposed the generic summarization method that extracts the most relevance sentences from the source document to form a summary. This method based on clustering of sentences. The specificity of this approach is that the generated summary can contain the main contents of different topics as many as possible and reduce its redundancy at the(More)
Multi-document summarization is a process of automatic creation of a compressed version of a given collection of documents that provides useful information to users. In this article we propose a generic multi-document summarization method based on sentence clustering. We introduce five clustering methods, which optimize various aspects of intra-cluster(More)
Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstrac-tive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the(More)
In this paper, we propose text summarization method that creates text summary by definition of the relevance score of each sentence and extracting sentences from the original documents. While summarization this method takes into account weight of each sentence in the document. The essence of the method suggested is in preliminary identification of every(More)