A Novel Gravity ‎Optimization Algorithm for Extractive Arabic Text Summarization

Main Article Content

Mustafa J. Hadi
https://orcid.org/0000-0002-5687-8942
Ayad R. Abbas
https://orcid.org/0000-0002-9797-421X
Osamah Y. Fadhil
https://orcid.org/0000-0003-1914-7160

Abstract

 


An automatic text summarization system mimics how humans summarize by picking the most ‎significant sentences in a source text. However, the complexities of the Arabic language have become ‎challenging to obtain information quickly and effectively. The main disadvantage of the ‎traditional approaches is that they are strictly constrained (especially for the Arabic language) by the ‎accuracy of sentence feature ‎functions, weighting schemes, ‎and similarity calculations. On the other hand, the meta-heuristic search approaches have a feature that tolerates imprecision, gets ‎prohibited results, and is not strictly bound by the above ‎restrictions. This ‎paper used the Gravitational Optimization Algorithm (GOA), a powerful metaheuristic ‎approach ‎based on the law of ‎gravity, to address the challenge of extractive summarizing Arabic texts. The objective function of the GOA algorithm is derived based on sentence significance, such as its length, ‎similarity degree, position, statistical term frequency, and named entity ownership. Essex Arabic ‎Summaries Corpus (EASC) was used to evaluate the proposed method and measured by the Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The proposed approach achieved 68.04% Recall, ‎‎58.49% Precision, and 60.05% F1-measure using ROUGE-1, higher than standard summarizers and metaheuristic approaches

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J. Hadi M, Abbas AR, Fadhil OY. A Novel Gravity ‎Optimization Algorithm for Extractive Arabic Text Summarization. Baghdad Sci.J [Internet]. 2024 Feb. 1 [cited 2024 Feb. 22];21(2):0537. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/7731
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References

Gupta V, Lehal GS. A Survey of Text Summarization Extractive Techniques. J Emerg Technol Web Intell. 2010; 2: 258-268. http://www.jetwi.us/uploadfile/2014/1226/20141226030617764.pdf

Mani K, Verma I, Meisheri H, Dey L. Multi-document summarization using distributed bag-of-words model. IEEE/WIC/ACM Int Conf Web Intell (WI). 2018; 672-675. https://doi.org/10.1109/WI.2018.00-14

To HQ, Nguyen KV, Nguyen NL-T, Nguyen AG-T. Monolingual versus Multilingual BeRTology for Vietnamese Extractive Multi-Document Summarization. arXiv preprint. arXiv. 2021. 2108.13741. https://doi.org/10.48550/arXiv.2108.13741

Al-Saleh AB, Menai MEB. Automatic Arabic text summarization: a survey. Artif Intell Rev. 2016; 45: 203–234. https://doi.org/10.1007/s10462-015-9442-x

Al Qassem LM, Wang D, Al Mahmoud Z, Barada H, Al-Rubaie A, Almoosa NI. Automatic Arabic summarization: a survey of methodologies and systems. Procedia Comput Sci. 2017; 117: 10–18. https://doi.org/10.1016/j.procs.2017.10.088

Mirshojaei SH, Masoomi B. Text summarization using cuckoo search optimization algorithm. J Comput Robot. 2015; 8: 19–24. https://jcr.qazvin.iau.ir/article_683_e08cfc39b8a850adf76246e0096d3d22.pdf

Hassan OF. Text summarization using ant colony optimization algorithm. Sudan University of Science and Technology, 2015. https://repository.sustech.edu/handle/123456789/11173

Sanchez-Gomez JM, Vega-Rodríguez MA, Pérez CJ. Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach. Knowl Based Syst. 2018; 159: 1–8. https://doi.org/10.1016/j.knosys.2017.11.029

Gamal M, El-Sawy A, AbuEl-Atta AH. Hybrid Algorithm Based on Chicken Swarm Optimization and Genetic Algorithm for Text Summarization. Int J Intell Eng Syst. 2021; 14: 319-331. http://www.inass.org/2021/2021063027.pdf

Jaradat YA, Al-Taani AT. Hybrid-based Arabic single-document text summarization approach using genatic algorithm. 2016 7th Int Conf Inf Commun Syst. 2016; 85–91. https://doi.org/10.1109/IACS.2016.7476091

Alwan MA, Onsi HM. A Proposed Textual Graph Based Model for Arabic Multi-document Summarization. Int J Adv Comput Sci Appl. 2016; 7: 435-439. https://dx.doi.org/10.14569/IJACSA.2016.070656

Azmi AM, Altmami NI. An abstractive Arabic text summarizer with user controlled granularity. Inf Process Manag. 2018: 54: 903–921. https://doi.org/10.1016/j.ipm.2018.06.002

Al-Maleh M, Desouki S. Arabic text summarization using deep learning approach. J Big Data. 2020; 7: 1–17. https://doi.org/10.1186/s40537-020-00386-7

Suleiman D, Awajan R. Deep learning based abstractive Arabic text summarization using two layers encoder and one layer decoder. J Theor Appl Inf Technol. 2020; 98: 3233-3244. file:///home/uu/Downloads/5Vol98No16.pdf

Al-Abdallah RZ, Al-Taani AT. Arabic single-document text summarization using particle swarm optimization algorithm. Procedia Comput Sci. 2017; 117: 30–37. https://doi.org/10.1016/j.procs.2017.10.091

Al-Abdallah RZ, Al-Taani AT. Arabic text summarization using firefly algorithm. Amity Int Conf Artif Intell 2019; 61–65. https://doi.org/10.1109/AICAI.2019.8701245

Qaroush A, Farha IA, Ghanem W, Washaha M, Maali E. An efficient single document Arabic text summarization using a combination of statistical and semantic features. J King Saud Univ- Comput Inf Sci. 2021; 33: 677-692. https://doi.org/10.1016/j.jksuci.2019.03.010

Al-Radaideh QA, Bataineh DQ. A Hybrid Approach for Arabic Text Summarization Using Domain Knowledge and Genetic Algorithms. Cognit Comput. 2018; 10: 651–669. https://doi.org/10.1007/s12559-018-9547-z

Ali ZH, Hussein AK, Abass HK, Fadel E. Extractive multi document summarization using harmony search algorithm. Telkomnika, Telecomm Comput, Electro Cont. 2021; 19: 89–95. http://doi.org/10.12928/telkomnika.v19i1.15766

Elmadani KN, Elgezouli M, Showk A. BERT Fine-tuning for Arabic Text Summarization. arXiv.2020; 2004. 14135. https://doi.org/10.48550/arXiv.2004.14135

Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inf Sci. 2009; 179: 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

Hassan AKA, Hadi MJ. Distributed Information Retrieval Based on Metaheuristic Search and Query Expansion. J Kufa Math Comput; 2017; 4.3: 4-11. https://doi.org/10.31642/JoKMC/2018/040302

Iqbal Z, Ilyas R, Chan HY, Ahmed N. Effective Solution of University Course Timetabling Using Particle Swarm Optimizer based Hyper Heuristic Approach. Baghdad Sci J. 2021; 18: 1465- 1475. https://doi.org/10.21123/bsj.2021.18.4(Suppl.).1465

Al-Behadili HNK. Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering. Baghdad Sci J. 2022; 19: 409- 421. https://doi.org/10.21123/bsj.2022.19.2.0409