IMPROVING EXTRACTIVE TEXT SUMMARIZATION VIA EFFICIENT COATI ALGORITHM FOR SINGLE DOCUMENT

Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

DOI Number
https://doi.org/10.2298/FUEE2503533R
First page
533
Last page
551

Abstract


In the digital era, the rapid expansion of online information demands efficient automated text summarization techniques to extract key insights from large documents. This study introduces a novel single-document extractive summarization approach that utilizes Term Frequency-Inverse Topic Frequency (TF-ITF) for feature extraction and the Coati Optimization Algorithm (COA) for optimal sentence selection. COA enhances summarization performance by balancing precision and recall through an adaptive fitness function, improving the quality of extracted summaries. The proposed model is evaluated on DUC 2002, 2003, and 2005 datasets using ROUGE, BLEU, precision, recall, and F1-score metrics. Comparative analysis against state-of-the-art optimization algorithms, including PSO, CSO, GWO, BCO, QABC, MCSO, and GLO, demonstrates that COA outperforms existing techniques, achieving higher recall and F1 scores while maintaining competitive precision. These findings establish COA as an effective optimization technique for enhancing automated text summarization.


Keywords

Term Frequency-Inverse Topic Frequency, Coati Optimization Algorithm, vectors, Single Document Text Summarization, Rouge scores, BLEU score

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References


W. Kryściński, N. S. Keskar, B. McCann, C. Xiong and R. Socher, "Neural Text Summarization: A Critical Evaluation", arXiv preprint, arXiv:1908.08960, 2019.

J. Weston, "A Neural Attention Model for Abstractive Sentence Summarization", arXiv preprint, arXiv:1509.00685, 2015.

L. Liu, Y. Lu, M. Yang, Q. Qu, J. Zhu and H. Li, "Generative Adversarial Network for Abstractive Text Summarization", In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, Apr. 2018, pp. 1-3.

D. Miller, "Leveraging BERT for Extractive Text Summarization on Lectures", arXiv preprint, arXiv:1906.04165, 2019.

K. Sarkar, "Automatic Single Document Text Summarization Using Key Concepts in Documents", J. Inf. Process. Syst., vol. 9, no. 4, pp. 602-620, 2013.

H. Christian, M. P. Agus and D. Suhartono, "Single Document Automatic Text Summarization Using Term Frequency-Inverse Document Frequency (TF-IDF)", ComTech: Comput. Math. Eng. Appl., vol. 7, no. 4, pp. 285-294, 2016.

R. Z. Al-Abdallah and A. T. Al-Taani, "Arabic Single-Document Text Summarization Using Particle Swarm Optimization Algorithm", Procedia Comput. Sci., vol. 117, pp. 30-37, 2017.

U. Rani and K. Bidhan, "Review Paper on Automatic Text Summarization", Int. Res. J. Eng. Technol. (IRJET), vol. 7, no. 4, pp. 3349-3354, 2020.

A. A. Syed, F. L. Gaol and T. Matsuo, "A Survey of the State-Of-The-Art Models in Neural Abstractive Text Summarization", IEEE Access, vol. 9, pp. 13248-13265, 2021.

S. Sivakumar and R. Rajalakshmi, "Context-aware Sentiment Analysis with Attention-Enhanced Features from Bidirectional Transformers", Soc. Netw. Anal. Min., vol. 12, no. 1, p. 104, 2022.

R. Srivastava, P. Singh, K. P. S. Rana and V. Kumar, "A Topic Modeled Unsupervised Approach to Single Document Extractive Text Summarization", Knowl.-Based Syst., vol. 246, p. 108636, 2022.

P. Verma, A. Verma and S. Pal, "An Approach for Extractive Text Summarization Using Fuzzy Evolutionary and Clustering Algorithms", Appl. Soft Comput., vol. 120, p. 108670, 2022.

D. V. P. Kumar, S. S. Raj, P. Verma and S. Pal, "Extractive Text Summarization Using Meta-Heuristic Approach", in FIRE (Working Notes), pp. 464-474, 2022.

J. Cheng and M. Lapata, "Neural Summarization by Extracting Sentences and Words", arXiv preprint, arXiv:1603.07252, 2016.

W. Kryściński, B. McCann, C. Xiong and R. Socher, "Evaluating the Factual Consistency of Abstractive Text Summarization", arXiv preprint, arXiv:1910.12840, 2019.

D. Debnath, R. Das and P. Pakray, "Extractive Single Document Summarization Using an Archive-Based Micro Genetic-2", In Proceedings of the 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2020, pp. 244-248.

T. Bezdan et al., "Hybrid Fruit-Fly Optimization Algorithm with k-Means for Text Document Clustering", Mathematics, vol. 9, no. 16, p. 1929, 2021.

D. Debnath, R. Das and P. Pakray, "Single Document Text Summarization Addressed with A Cat Swarm Optimization Approach", Appl. Intell., vol. 53, no. 10, pp. 12268-12287, 2023.

S. P. Patil and R. Rautray, "SMATS: Single and Multi Automatic Text Summarization", Karbala Int. J. Modern Sci., vol. 9, no. 1, p. 6, 2023.

K. Svore, L. Vanderwende and C. Burges, "Enhancing Single-Document Summarization by Combining Ranknet and Third-Party Sources", In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 2007, pp. 448-457.

S. Mandal, G. K. Singh and A. Pal, "Single Document Text Summarization Technique Using Optimal Combination of Cuckoo Search Algorithm, Sentence Scoring and Sentiment Score", Int. J. Inf. Technol., vol. 13, no. 5, pp. 1805-1813, 2021.

A. Jain, D. Yadav and A. Arora, "Particle Swarm Optimization for Punjabi Text Summarization", Int. J. Oper. Res. Inf. Syst. (IJORIS), vol. 12, no. 3, pp. 1-17, 2021.

S. H. Apandi, J. Sallim, R. Mohamed and N. Ahmad, "Data Pre-Processing of Website Browsing Records: To Prepare Quality Dataset for Web Page Classification", JOIV: Int. J. Inf. Visual., vol. 8, no. 1, pp. 239-246, 2024.

M. Jaiswal and S. Das, "Detecting Spam E-Mails Using Stop Word TF-IDF and Stemming Algorithm with Naïve Bayes Classifier on the Multicore GPU", Int. J. Electr. Comput. Eng., vol. 11, no. 4, pp. 3168-3175, 2021.

K. K. Mohbey and S. Tiwari, "Preprocessing and Morphological Analysis in Text Mining", Int. J. Electron. Commun. Comput. Eng., vol. 2, no. 2, pp. 116-122, 2011.

Z. Gou, Z. Huo, Y. Liu and Y. Yang, "A Method for Constructing Supervised Topic Model Based on Term Frequency-Inverse Topic Frequency", Symmetry, vol. 11, no. 12, p. 1486, 2019.

A. K. Singh and M. Shashi, "Vectorization of Text Documents for Identifying Unifiable News Articles", Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 7, pp. 305-310, 2019.

M. Dehghani, Z. Montazeri, E. Trojovská and P. Trojovský, "Coati Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems", Knowl.-Based Syst., vol. 259, p. 110011, 2023.

V. Dalal and L. Malik, "Semantic Graph-Based Automatic Text Summarization for Hindi Documents Using Particle Swarm Optimization", in Information and Communication Technology for Intelligent Systems, Springer, pp. 284-289, 2018.

N. Saini, S. Saha, A. Jangra and P. Bhattacharyya, "Extractive Single Document Summarization Using Multi-Objective Optimization: Exploring Self-Organized Differential Evolution, Grey Wolf Optimizer and Water Cycle Algorithm", Knowl.-Based Syst., vol. 164, pp. 45-67, 2019.

J. Rautaray et al., "SEQABC: Revolutionizing Single Document Extractive Text Summarization with Quick Artificial Bee Colony", Nanotechnol. Percept., pp. 737-750, 2024.

M. Mendoza, C. Cobos and E. León, "Extractive Single-Document Summarization Based on Global-Best Harmony Search and A Greedy Local Optimizer", in Advances in Artificial Intelligence and Its Applications, Springer, pp. 52-66, 2015.

H. Zhang, P. S. Yu and J. Zhang, "A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models", arXiv preprint, arXiv:2406.11289, 2024.

D. Yadav, J. Desai and A. K. Yadav, "Automatic Text Summarization Methods: A Comprehensive Review", arXiv preprint, arXiv:2204.01849, Mar. 2022.

S. Mirjalili, S. M. Mirjalili and A. Lewis, "Grey Wolf Optimizer", Adv. Eng. Soft., vol. 69, pp. 46-61, 2014.

D. Karaboga and B. Basturk, "A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm", J. Glob. Optim., vol. 39, no. 3, pp. 459-471, 2007.

G. Wang, S. Deb and L. Zhao, "A Multi-Colony Multi-Objective Particle Swarm Optimizer for Dynamic Optimization Problems", Eng. Appl. Artif. Intell., vol. 62, pp. 3-15, 2017.

S. Sharma, A. Verma and P. K. Shukla, "A Novel Glowworm Swarm Optimization Algorithm for Text Document Summarization", Expert Syst. Appl., vol. 160, p. 113653, 2020.

X. Yuan, Y. Xu, L. Gao and Y. Zhang, "A Quick Artificial Bee Colony Algorithm for Large-Scale Numerical Optimization", Appl. Soft Comput., vol. 48, pp. 579-596, 2016.


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