Global Journal of Computer Sciences: Theory and Research Volume 10, Issue 2, (2020) 48-56 www.gjcs.eu Single document summarisation based on Grey Wolf Optimisation Moein Salimi Sartakhti*, Amirkabir University, Department of Computer Engineer, Tehran, Iran Ahmad Yoosofan, University of Kashan, Electrical and Computer Engineering Department, Kashan, Iran Ali Asghar Fatehi, University of Kashan, Electrical and Computer Engineering Department, Kashan, Iran Ali Rahimid, VIT University, School of Social Sciences and Languages, Vellore 632014, India Suggested Citation: Sartakhti, S. M, Yoosofan, A., Fatehi, A. & Rahimid, A. (2020) Single document summarisation based on Grey Wolf Optimisation. Global Journal of Computer Sciences: Theory and Research. 10(2), 48-030. https://doi.org/10.18844/gjcs.v10i2.5807 Received June 30, 2020; revised from August 15, 2020; accepted from October 17, 2020. Selection and peer review under responsibility of Prof. Dr. Dogan Ibrahim, Near East University, Cyprus. ©2020 Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi. All rights reserved. Abstract 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. * ADDRESS FOR CORRESPONDENCE: Moein Salimi Sartakhti, Department of Computer Engineer, Amirkabir University, Tehran, Iran. E-mail address: artakhti.salimi@gmail.com