A Keyword Extraction Approach for Single Document Extractive Summarization Based on Topic Centrality

Graph based keyword extraction approaches represent the text document as a graph with the words as vertices. An edge between two vertices exists only if the corresponding words are related. The effectiveness of the graph representation is dependent on how the relationship is defined between two words. Early approaches used word order, word co-occurrence and syntactic relationships. Recent graph representation approaches are using lexical association measures for defining relationship between two words. The existing relationships can only help in identifying the words which form the topics in a document. But the topics in a document essentially form a theme in the document. Theme is the central idea conveyed though the topics in a document. In this paper a new relationship is defined between the words to capture the words which convey the theme of the document. This relationship is defined using lexical association among the words in the document. Based on the relationship a graph representation of text is defined and used for extracting the words that build the theme of the document. These words are used for selecting important sentences in the document. Experiments on DUC 2002 data set indicated that the proposed keyword extraction approach improves the quality of Extractive summarization.