Extractive Text Summarization via Graph Entropy


Hark C., Uçkan T. , Seyyarer E. , KARCI A.

International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 21 - 22 September 2019 identifier identifier

Abstract

There is growing interest in automatic summarizing systems. This study focuses on a subtractive, general and unsupervised summarization system. It is provided to represent the texts to be summarized with graphs and then graph entropy is used to interpret the structural stability and structural information content on the graphs representing the text files. The performance of the proposed text summarizing approach for the purpose of summarizing the text on the data set of Document Understanding Conference (DUC-2002) including open access texts and summaries of these texts was calculated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluation metrics. Experimental processes were repeated for 200 and 400 word abstracts. Experimental results reveale that the proposed text summarizing system performs competitively with competitive methods for different ROUGE metrics.