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Computers and Computing

Automatic Text Summarization of Konkani Folk Tales Using Supervised Machine Learning Algorithms and Language Independent Features

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  • A. Qaroush, I. Abu Farha, W. Ghanem, M. Washaha, and E. Maali, “An efficient single document Arabic text summarization using a combination of statistical and semantic features,” J. King Saud Univ. Comp. Inform. Sci., 2019. DOI:10.1016/j.jksuci.2019.03.010.
  • N. Moratanch, and S. Chitrakala. “A survey on extractive text summarization,” in IEEE International Conference on Computer, Communication and Signal Processing (ICCCSP), 2017. DOI:10.1109/icccsp.2017.7944061.
  • N. Andhale, and L. A. Bewoor. “An overview of Text Summarization techniques,” in International Conference on Computing Communication Control and Automation (ICCUBEA), 2016. DOI:10.1109/iccubea.2016.7860024.
  • A. Dey, “Machine learning algorithms: a review,” Int. J. Comp. Sci. Inform. Technol., Vol. 7, no. 3, pp. 1174–1179, 2016. ISSN: 0975-9646.
  • M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A systematic review on supervised and unsupervised machine learning algorithms for data science,” in Supervised and unsupervised learning for data science. Unsupervised and semi-supervised learning, M. Berry, A. Mohamed, and B. Yap, Eds. Cham: Springer, 2020, pp. 3-21.
  • J. D’Silva, and U. Sharma, “Automatic text summarization of Indian languages: a multilingual problem,” J. Theor. Appl. Inform. Technol., Vol. 97, no. 1, pp. 3026-3037, 2019, ISSN: 1992-8645.
  • J. E. T. Akinsola, “Supervised machine learning algorithms: classification and comparison,” Int. J. Comp. Trends Technol., Vol. 48, no. 3, pp. 128–138, June 2017. DOI:10.14445/22312803/IJCTT-V48P126.
  • W. S. El-Kassas, C. R. Salama, A. Rafea, and H. K. Mohamed, “Automatic text summarization: a comprehensive survey,” Expert Syst. Appl., Vol. 165, pp. 113679, 2021.
  • S. Kumbhar, A. Bordia, S. Kapse, and F. Paatil, “Comparison of extractive text summarization methods,” Int. J. Advan. Electr. Comp. Sci., Vol. 5, no. 7, pp. 60-62, July 2018. ISSN: 2393-2835.
  • A. Nenkova, and K. McKeown, “Automatic summarization, foundations and trends,” Inf. Retr. Boston., Vol. 5, no. 2–3, pp. 103–233, 2011. DOI:10.1561/1500000015.
  • J. Kupiec, J. Pedersen, and F. Chen. “A Trainable document summarizer,” in Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1995, pp. 68–73.
  • H. P. Luhn, “The automatic creation of literature abstracts,” IBM J. Res. Dev., Vol. 2, no. 2, pp. 159–165, April 1958.
  • H. P. Edmundson, “New methods in automatic extracting,” J. ACM, Vol. 16, no. 2, pp. 264–285, 1969.
  • C. D. Paice, “Constructing literature abstracts by computer: techniques and prospects,” Inform. Process. Manage., Vol. 26, no. 1, pp. 171–186, 1990.
  • J. M. Conroy, and D. P. O’Leary. “Text summarization via hidden Markov models,” in Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001, pp. 406–407.
  • D. Hakkani-Tur, and G. Tur. “Statistical sentence extraction for information distillation,” in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’07, 2007. DOI:10.1109/icassp.2007.367148.
  • E. Hovy, and C. Y. Lin, “Automated text summarization in SUMMARIST,” in Advances in Automatic Text Summarization, I. Mani, M. T. Maybury Eds. Cambridge: MIT Press, pp. 82–94, 1999.
  • J. Leskovec, N. Milic-Frayling, and M. Grobelnik. “Impact of linguistic analysis on the semantic graph coverage and learning of document extracts,” in Proceedings of the National Conference on Artificial Intelligence, 2005, pp. 1069–1074.
  • M. Osborne. “Using maximum entropy for sentence extraction,” in Proceedings of the ACL Workshop on Automatic Summarization, 2002, pp. 1–8.
  • L. Zhou, and E. Hovy. “A web-trained extraction summarization system,” in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, 2003, pp. 205–211.
  • S. Xie, and Y. Liu. “Using corpus and knowledge-based similarity measure in Maximum Marginal Relevance for meeting summarization,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, pp. 4985–4988.
  • M. Fuentes, E. Alfonseca, and H. Rodrıguez. “Support vector machines for query-focused summarization trained and evaluated on pyramid data,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics, Companion Volume: Proceedings of the Demo and Poster Sessions, 2007, pp. 57–60.
  • M. A. Fattah, and F. Ren, “GA, MR, FFNN, PNN and GMM based models for automatic text summarization,” Comput. Speech. Lang., Vol. 23, no. 1, pp. 126–144, 2009. DOI:10.1016/j.csl.2008.04.002.
  • R. Belkebir, and A. Guessoum, “A supervised approach to Arabic text summarization using AdaBoost,” in New contributions in information systems and technologies, advances in intelligent systems and computing, Vol. 353, A. Rocha, A. Correia, S. Costanzo, and L. Reis, Eds. Cham: Springer, 2015, pp. 227–236.
  • N. Desai, and P. Shah, “Automatic text summarization using supervised machine learning technique for Hindi language,” Int. J. Res. Eng. Technol., Vol. 05, no. 06, pp. 361–367, Jun-2016.
  • R. Nallapati, F. Zhai, and B. Zho. “SummaRuNNer: a recurrent neural network based sequence model for extractive summarization of documents,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, (AAAI'17), Press, San Francisco, California, USA, 2017, pp. 3075–3081.
  • S. Narayan, N. Papasarantopoulos, S. B. Cohen, and M. Lapata. “Neural extractive summarization with side information,” ArXiv, abs/1704.04530, 2017.
  • A. Sinha, A. Yadav, and A. Gahlot. “Extractive text summarization using neural networks,” arXiv preprint arXiv:1802.10137, 2018.
  • E. Brito, M. Lübbering, D. Biesner, L. P. Hillebrand, and C. Bauckhage. “Towards supervised extractive text summarization via RNN-based sequence classification,” arXiv:1911.06121, 2019.
  • X. Mao, H. Yang, S. Huang, Y. Liu, and R. Li, “Extractive summarization using supervised and unsupervised learning,” Expert Syst. Appl., Vol. 133, pp. 173–181, 2019.
  • D. Krishnan, P. Bharathy, Anagha, and M. Venugopalan. “A supervised approach for extractive text summarization using minimal robust features,” in 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 2019, pp. 521–527. DOI:10.1109/ICCS45141.2019.9065651.
  • M. E. Hannah, “A classification-based summarization model using supervised learning,” in Computational network application tools for performance management, asset analytics (performance and safety management), M. Pant, T. Sharma, S. Basterrech, and C. Banerjee, Eds. Singapore: Springer, 2020. DOI:10.1007/978-981-32-9585-8_9.
  • R. Bhargava, and Y. Sharma, “Deep extractive text summarization,” Proc. Comput. Sci., Vol. 167, pp. 138–146, 2020. DOI:10.1016/j.procs.2020.03.191. ISSN 1877-0509.
  • O. Klymenko, D. Braun, and F. Matthes. “Automatic text summarization: a state-of-the-art review.” ICEIS, 2020.
  • A. Géron. Hands-on machine learning with Scikit-learn and tensorflow. Sebastopol, CA: O’Reilly Media, Inc, 2017.
  • J. D’Silva, and U. Sharma, “Unsupervised automatic text summarization of Konkani texts using K-means with elbow method,” Int. J. Eng. Res. Technol., Vol. 13, no. 9, pp. 2380–2384, 2020. ISSN 0974-3154.
  • Statement – 4: Distribution of Population by Schedule and Other Languages India, States and Union Territories. Office of the Registrar General & Census Commissioner, India, Ministry of Home Affairs, Government of India. 2011. https://censusindia.gov.in/2011Census/Language-2011/Statement-4.pdf. Accessed November 15, 2020.
  • J. D’Silva, and U. Sharma, “Development of a Konkani language dataset for automatic text summarization and its challenges,” Int. J. Eng. Res. Technol., Vol. 12, no. 10, pp. 1813–18917, 2019. ISSN 0974-3154.
  • V. Nasteski, “An overview of the supervised machine learning methods,” Horizons B, Vol. 4, pp. 51–62, Dec 2017. DOI:10.20544/HORIZONS.B.04.1.17.P05.
  • S. A. A. Zaidi, and S. M. Hassan. “Urdu/Hindi News Headline, text classification by using different machine learning algorithms.” DOI:10.13140/RG.2.2.12068.83846.
  • B. M. Hsu, “Comparison of supervised classification models on textual data,” Mathematics, Vol. 8, no. 5, pp. 851, 2020. DOI:10.3390/math8050851.
  • S. Almatarneh, and P. Gamallo, “Comparing supervised machine learning strategies and linguistic features to search for very negative opinions,” Information, Vol. 10, no. 1, pp. 16, Jan 2019.
  • M. Z. Islam, J. Liu, J. Li, L. Liu, and W. Kang. “A semantics aware random forest for text classification,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, November 2019, pp. 1061–1070.
  • B. Frénay, and M. Verleysen, “Classification in the presence of label noise: a survey,” IEEE Trans. Neural Netw. Learn. Syst., Vol. 25, no. 5, pp. 845–869, 2013.
  • M. Litvak, and M. Last, “Cross-lingual training of summarization systems using annotated corpora in a foreign language,” Inf. Retr. Boston., Vol. 16, no. 06, pp. 629–656, September 2013. DOI:10.1007/s10791-012-9210-3.
  • P. B. Baxendale, “Machine-made index for technical literature – an experiment,” IBM J. Res. Dev., Vol. 2, no. 4, pp. 354–361, Oct. 1958.
  • C. Y. Lin, and E. Hovy. “Identifying topics by position,” Proceedings of the Fifth Conference on Applied Natural Language Processing, 1997, pp. 283–290.
  • C. Nobata, S. Sekine, M. Murata, K. Uchimoto, M. Utiyama, and H. Isahara. “Sentence extraction system assembling multiple evidence,” Proceedings of 2nd NTCIR workshop, 2001, pp. 319–324.
  • L. Vanderwende, H. Suzuki, C. Brockett, and A. Nenkova, “Beyond SumBasic: task-focused summarization with sentence simplification and lexical expansion,” Inf. Process. Manag., Vol. 43, pp. 1606–1618, November 2007. DOI:10.1016/j.ipm.2007.01.023.
  • J. Larocca Neto, A. D. Santos, C. A. A. Kaestner, and A. A. Freitas. “Generating text summaries through the relative importance of topics,” Lecture Notes in Computer Science, Berlin Heidelberg 2000, pp. 300–309. DOI:10.1007/3-540-44399-1_31.
  • M. Litvak, M. Last, and M. Friedman. “A new approach to improving multilingual summarization using a genetic algorithm,” ACL’10: Proceedings of the 48th annual meeting of the association for computational linguistics, Uppsala, Sweden, July 2010, pp. 927–936.
  • F. Pedregosa et al., “Scikit-learn: machine learning in python,” J. Mach. Learn. Res., Vol. 12, pp. 2825–2830, 2011.
  • T. Joachims. “Making large-scale SVM learning practical,” Technical report, SFB 475: Komplexitätsreduktion Multivariaten Datenstrukturen, Univ. Dortmund, Dortmund, Tech. Rep., p. 28, 1998.
  • S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Med. Inform. Decis. Mak., Vol. 19, no. 1, pp. 1–16, 2019. DOI:10.1186/s12911-019-1004-8.
  • C. Y. Lin. “ROUGE: A package for automatic evaluation of summaries,” in Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain, July 2004, pp. 74–81.
  • A. Nenkova. “Automatic text summarization of newswire: lessons learned from the document understanding conference,” in Proceedings of the 20th National Conference on Artificial Intelligence, Vol, 3, AAAI’05, Pittsburgh, Pennsylvania. AAAI Press, 2005, pp. 1436–1441.
  • R. Campos, V. Mangaravite, A. Pasquali, A. Jorge, C. Nunes, and A. Jatowt, “YAKE! Keyword extraction from single documents using multiple local features,” Inform. Sci. J., Vol. 509, pp. 257–289, 2020. DOI:10.1016/j.ins.2019.09.013. ISSN 0020-0255.

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