Dominating set-based extractive text summarization in graphs

In this study, a new method has been proposed extractive document summarization. In the extractive text summarization method, sentences are selected from the main text as they are to generate the summary. Selecting sentences that contain the maximum amount of information from the text to be summarized increases the success rate of generating the summary. In this thesis study, Karci Dominating Set Algorithm was used. A graph was created from the adjacency matrix generated based on the common word counts of the sentences belonging to the text to be summarized. The sentences represented by the nodes in the dominating set of the graph were removed from the main text, and a new graph was created from the remaining sentences. The summary was obtained based on the eigenvector centrality values of this new graph. The research utilized the Document Understanding Conference (DUC-2002 and DUC-2004) dataset for evaluation. Performance assessment was conducted using ROUGE evaluation metrics, and the results were compared against other competitive methods. The experimental procedures were repeated for summaries of 100, 200, and 400 words. The outcomes obtained with the proposed method clearly demonstrate the contributions of this innovative approach.