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In this paper, we present an algorithm for extracting keywords representing the asserted main point in a document, without relying on external devices such as natural language processing tools or a document corpus. Our algorithm KeyGraph is based on the segmentation of a graph, representing the co-occurrence between terms in a document, into {\it clusters}. Each cluster corresponds to a concept on which author's idea is based, and top ranked terms by a statistic based on each term's relationship to these clusters are selected as keywords. This strategy comes from considering that a document is constructed like a building for expressing new ideas based on traditional concepts.The experimental results show that thus extracted terms match author's point quite accurately, even though KeyGraph does not use each term's average frequency in a corpus, i.e., KeyGraph is a content-sensitive, domain independent device of indexing.