Single Document Summarization Based on Grey Wolf Optimization

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Moein Salimi Sartakhti
Ahmad Yoosofan
Ali Asghar Fatehi
Ali Rahimi


The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by Grey Wolf Optimiser (GWO). Optimisation of features gives better results than using individual features. This is the first attempt to show the performance of GWO for Persian text summarisation. The proposed method is compared with the genetic algorithm and the evolutionary strategy. The results show that our model will be useful in this research area.


Keywords: Text summarisation, genetic algorithm, sentence, score function, evolutionary strategy.


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How to Cite
Sartakhti, M. S. ., Yoosofan, A. ., Fatehi, A. A., & Rahimi, A. (2020). Single Document Summarization Based on Grey Wolf Optimization. Global Journal of Computer Sciences: Theory and Research, 10(2), 48–56.