  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. Document
summarization is a process of automatically creating a compressed version of a
given document that provides useful information to users, and multi-document
summarization is to produce a summary delivering the majority of information
content from a set of documents about an explicit or implicit main topic. The
lexical cohesion structure of the text can be exploited to determine the
importance of a sentence/phrase. Lexical chains are useful tools to analyze the
lexical cohesion structure in a text .In this paper we consider the effect of
the use of lexical cohesion features in Summarization, And presenting a
algorithm base on the knowledge base. Ours algorithm at first find the correct
sense of any word, Then constructs the lexical chains, remove Lexical chains
that less score than other, detects topics roughly from lexical chains,
segments the text with respect to the topics and selects the most important
sentences. The experimental results on an open benchmark datasets from DUC01
and DUC02 show that our proposed approach can improve the performance compared
to sate-of-the-art summarization approaches.
