Abstract
In the past few decades, the automatic text summarization process has become an essential area in research. The automatic text summarization process extracts the essential and the most-resourceful information the users need, and various users in many fields easily handle it. This work extracts linear and non-linear information using a Support Vector Machine (SVM). Then, the extracted features are given into the Bag of Features (BoF) which is provided as an input to the classifier model known as Multi-layered CNN for feature representation. This process is known as BoF-CNN. The multiple layers of the CNN model analyse the given features and provide the weighted score for the keywords based on the higher needs. The importance/classification is provided. The combinations of features are weighted with the graph model when placed in BoF. The proposed model effectively analyses the features and classifies the text based on weight. The simulation is done in MATLAB 2020a environment and provides better results when analysing incoming data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not applicable.
References
Abdulateef S, Khan NA, Chen B, Shang X (2020) Multidocument Arabic text summarization based on clustering and Word2Vec to reduce redundancy. Information 11(2):59
Al-Abdallah RZ, Al-Taani AT (2017) Arabic single-document text summarization using particle swarm optimization algorithm. Procedia Comput Sci 117:30–37
Al-Radaideh QA, Bataineh DQ (2018) A hybrid approach for Arabic text summarization using domain knowledge and genetic algorithms. Cognit Comput 10(4):651–669
Al-Sabahi K, Zhang Z, Long J, Alwesabi K (2018) An enhanced latent semantic analysis approach for Arabic document summarization. Arabian J Sci Eng 43(12):8079–8094
Ayedh A, Tan G, Alwesabi K, Rajeh H (2016) The effect of pre-processing on Arabic document categorization. Algorithms 9(2):27
Cardoso PC, Pardo TA (2016) Multi-document summarization using semantic discourse models. Procesamiento Del Lenguaje Nat 56:57–64
Dong Y (2018) A survey on neural network-based summarization methods. arXiv:1804.04589. [Online]. Available: http://arxiv.org/ abs/1804.04589
Elbarougy R, Behery G, El Khatib A (2020) A proposed natural language processing preprocessing procedures for enhancing Arabic text summarization. In: Abd Elaziz M, Al-qaness M, Ewees A, Dahou A (eds) Recent advances in NLP: the case of Arabic language studies in computational intelligence. Springer, Cham
Hassan NH, Al-Kabi I, Mahmoud M, Issa MB (2016) Automatic keyphrase extractor from arabic documents. Int J Adv Comput Sci Appl 7(2):192–199
Hernandez-Castaneda A, Garcia-Hernandez RA, Ledeneva Y, Millan-Hernandez CE (2020) Extractive automatic text summarization based on lexical-semantic keywords. IEEE Access 8:49896–49907
Jung C, Datta R, Segev A (2017) Multi-document summarization using evolutionary multi-objective optimization. In: Proceedings of the genetic and evolutionary computation conference companion, pp. 31–32.
Liu Y, Lapata M (2019) Text summarization with pre-trained encoders. In: Proceedings Conference Empirical Methods Natural Lang. Process, 9th International Joint Conference Natural Lang. Process. (EMNLP-IJCNLP), pp. 3721–3731.
Meena YK, Gopalani D (2016) Efficient voting-based extractive automatic text summarization using prominent feature set. IETE J Res 62(5):581–590
Mustafa M, Eldeen AS, Bani-Ahmad S, Elfaki AO (2017) A comparative survey on Arabic stemming: approaches and challenges. Intell Inf Manage 9(2):39–67
Nagi J, Caro GAD, Giusti A, Nagi F, Gambardella LM (2012) Convolutional neural support vector machines: Hybrid visual pattern classifiers for multi-robot systems. In: 2012 11th International Conference on Machine Learning and Applications, pp. 27–32.
Patel D, Shah S, Chhinkaniwala H (2019) Fuzzy logic based multi-document summarization with improved sentence scoring and redundancy removal technique. Expert Syst Appl 134:167–177
Qaroush A, Farha IA, Ghanem W, Washaha M, Maali E (2021) An efficient single document Arabic text summarization using a combination of statistical and semantic features. J King Saud Univ Comput Inform Sci 33(6):677–692
Qi W, Yan Y, Gong Y, Liu D, Duan N, Chen J, Zhang R, Zhou M (2020) ProphetNet: Predicting future N-gram for sequence-to sequence pre-training. arXiv:2001.04063. [Online]. Available: http://arxiv.org/abs/2001.04063
Qumsiyeh R, Ng YK (2016) Searching web documents using a summarization approach. Int J Web Inform Syst. https://doi.org/10.1108/IJWIS-11-2015-0039
Rautray R, Balabantaray RC (2018) An evolutionary framework for multi-document summarization using cuckoo search approach: MDSCSA. Appl Comput Information 14(2):134–144
Sherubha P, Mohanasundaram N (2019a) An efficient network threat detection and classification method using ANP-MVPS algorithm in wireless sensor networks. Int J Innov Technol Explor Eng 8(11):1597–1606
Sherubha P, Mohanasundaram N (2019b) An efficient intrusion detection and authentication mechanism for detecting clone attack in wireless sensor networks. J Adv Res Dyn Control Syst 11(5):55–68
Sherubha P, Sasirekha SP, Manikandan V, Gowsic K, Mohanasundaram N (2020) Graph based event measurement for analyzing distributed anomalies in sensor networks. Sādhanā 45(1):1–5. https://doi.org/10.1007/s12046-020-01451-w
Vijaymeena MK, Kavitha K (2016) A survey on similarity measures in text mining. Mach Learn Appl Int J 3(2):19–28
Wang, C. Li, W. Wang, Y. Zhang, D. Shen, X. Zhang, R. Henao, and L. Carin (2018) Joint embedding of words and labels for text classification,’’ In: Proceedings 56th Annual Meeting Association Computing Linguistics, p. 2321
Wang D, Liu P, Zheng Y, Qiu X, Huang X (2020) Heterogeneous graph neural networks for extractive document summarization. In: Proceedings 58th Annual Meeting Association Computing Linguistics 2020.
Widyassari AP, Rustad S, Shidik GF, Noersasongko E, Syukur A, Affandy A (2020) Review of automatic text summarization techniques & methods. J King Saud Univ Comput Inform Sci 34(4):1029–1046
Yang SJH, Huang JCH, Huang AYQ, Chen IYL (2016) MOOCs for K-12 and higher education in Taiwan. In: Proceedings 5th IIAI Int. Congr. Adv. Appl. Information. (IIAI-AAI), pp. 361–365.
Zhong M, Liu P, Chen Y, Wang D, Qiu X, Huang X (2020) Extractive summarization as text matching. arXiv:2004.08795. [Online]. Available: http://arxiv.org/abs/2004.08795
Funding
No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors are contributed equally.
Corresponding author
Ethics declarations
Conflict of interest
Not applicable.
Ethical approval
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Malarselvi, G., Pandian, A. Multi-layered network model for text summarization using feature representation. Soft Comput 27, 311–322 (2023). https://doi.org/10.1007/s00500-022-07617-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07617-4