A Novel Approach for Graph-based Extractive Text Summarization using Karcı Dominant Set Algorithm and Eigenvector Centrality

With the rapid increase in textual data sources in recent years, it is seen that many studies have been carried out in the field of automatic text summarization. In this study, a new method is proposed for graph-based extractive text summarization. In addition, within the scope of the study, Karcı Dominant Cluster Algorithm is used for the first time in text summarization systems. In the proposed method, firstly, a graph is created from the neighborhood matrix based on the common word numbers of the sentences belonging to the text to be summarized. In the second step, the sentences represented by the nodes in the dominant cluster of the graph are determined using the Karcı Dominant Set Algorithm. In the third step, a new graph is created from the remaining sentences by removing the sentences belonging to the dominant clusters determined from the main text. According to the eigenvector centrality values of the new graph created in the last step, the central sentences were found and the sentences were selected to start with the most valuable sentence and summaries were obtained. The study was carried out on the Document Understanding Conference (DUC-2002 and DUC-2004) dataset. Its performance was calculated with ROUGE evaluation metrics and the results were compared with other competitive methods. The developed model reached a ROUGE performance value of 0.35748 for a 100-word summary, 0.49049 for a 200-word summary, and 0.57586 for a 400-word summary. The values reported during the experimental processes of the study clearly reveal the contribution of this innovative method to the literature.