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The technology of automatic document summarization is maturing and mayprovide a solution to the information overload problem. Nowadays, documentsummarization plays an important role in information retrieval. With a largevolume of documents, presenting the user with a summary of each documentgreatly facilitates the task of finding the desired documents. Documentsummarization is a process of automatically creating a compressed version of agiven document that provides useful information to users, and multi-documentsummarization is to produce a summary delivering the majority of informationcontent from a set of documents about an explicit or implicit main topic. Thelexical cohesion structure of the text can be exploited to determine theimportance of a sentence/phrase. Lexical chains are useful tools to analyze thelexical cohesion structure in a text .In this paper we consider the effect ofthe use of lexical cohesion features in Summarization, And presenting aalgorithm base on the knowledge base. Ours algorithm at first find the correctsense of any word, Then constructs the lexical chains, remove Lexical chainsthat less score than other, detects topics roughly from lexical chains,segments the text with respect to the topics and selects the most importantsentences. The experimental results on an open benchmark datasets from DUC01and DUC02 show that our proposed approach can improve the performance comparedto sate-of-the-art summarization approaches.

Automated Text Summarization Base on Lexicales Chain and graph Using of WordNet and Wikipedia Knowledge Base