Cite

[1] M. H. Aghdam, S. Heidari, Feature selection using particle swarm optimization in text categorization, Journal of Artificial Intelligence and Soft Computing Research 5 (4) (2015) 231–238.10.1515/jaiscr-2015-0031 Search in Google Scholar

[2] G. C. V. Vilca, M. A. S. Cabezudo, A study of abstractive summarization using semantic representations and discourse level information (2017) 482–490.10.1007/978-3-319-64206-2_54 Search in Google Scholar

[3] N. Moratanch, S. Chitrakala, A survey on extractive text summarization, in: 2017 international conference on computer, communication and signal processing (ICCCSP), IEEE, 2017, pp. 1–6.10.1109/ICCCSP.2017.7944061 Search in Google Scholar

[4] M. Gambhir, V. Gupta, Recent automatic text summarization techniques: a survey, Artificial Intelligence Review 47 (1) (2017) 1–66. Search in Google Scholar

[5] K. Yang, K. Al-Sabahi, Y. Xiang, Z. Zhang, An integrated graph model for document summarization, Information 9 (9) (2018) 232.10.3390/info9090232 Search in Google Scholar

[6] R. M. Alguliyev, R. M. Aliguliyev, N. R. Isazade, A. Abdi, N. Idris, Cosum: Text summarization based on clustering and optimization, Expert Systems 36 (1) (2019) e12340.10.1111/exsy.12340 Search in Google Scholar

[7] E. Lloret, M. T. Romá-Ferri, M. Palomar, Compendium: A text summarization system for generating abstracts of research papers, Data & Knowledge Engineering 88 (2013) 164–175. Search in Google Scholar

[8] C. Fang, D. Mu, Z. Deng, Z. Wu, Word-sentence co-ranking for automatic extractive text summarization, Expert Systems with Applications 72 (2017) 189–195.10.1016/j.eswa.2016.12.021 Search in Google Scholar

[9] M. A. Fattah, F. Ren, Ga, mr, ffnn, pnn and gmm based models for automatic text summarization, Computer Speech & Language 23 (1) (2009) 126–144.10.1016/j.csl.2008.04.002 Search in Google Scholar

[10] D. Shen, J.-T. Sun, H. Li, Q. Yang, Z. Chen, Document summarization using conditional random fields., in: IJCAI, Vol. 7, 2007, pp. 2862–2867. Search in Google Scholar

[11] R. Nallapati, F. Zhai, B. Zhou, Summarunner: A recurrent neural network based sequence model for extractive summarization of documents, in: Thirty-first AAAI conference on artificial intelligence, 2017.10.1609/aaai.v31i1.10958 Search in Google Scholar

[12] O. Vikas, A. K. Meshram, G. Meena, A. Gupta, Multiple document summarization using principal component analysis incorporating semantic vector space model (2008) 141–156. Search in Google Scholar

[13] J.-H. Lee, S. Park, C.-M. Ahn, D. Kim, Automatic generic document summarization based on non-negative matrix factorization, Vol. 45, Elsevier, 2009, pp. 20–34.10.1016/j.ipm.2008.06.002 Search in Google Scholar

[14] W. S. El-Kassas, C. R. Salama, A. A. Rafea, H. K. Mohamed, Automatic text summarization: A comprehensive survey, Expert Systems with Applications 165 (2021) 113679.10.1016/j.eswa.2020.113679 Search in Google Scholar

[15] D. Sahoo, R. Balabantaray, M. Phukon, S. Saikia, Aspect based multi-document summarization, in: 2016 International Conference on Computing, Communication and Automation (ICCCA), IEEE, 2016, pp. 873–877.10.1109/CCAA.2016.7813838 Search in Google Scholar

[16] M. J. Mohan, C. Sunitha, A. Ganesh, A. Jaya, A study on ontology based abstractive summarization, Procedia Computer Science 87 (2016) 32–37.10.1016/j.procs.2016.05.122 Search in Google Scholar

[17] M. Mohd, R. Jan, M. Shah, Text document summarization using word embedding, Expert Systems with Applications 143 (2020) 112958.10.1016/j.eswa.2019.112958 Search in Google Scholar

[18] D. D. Lee, H. S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature 401 (6755) (1999) 788.10.1038/4456510548103 Search in Google Scholar

[19] M. H. Aghdam, M. D. Zanjani, A novel regularized asymmetric non-negative matrix factorization for text clustering, Information Processing & Management 58 (6) (2021) 102694.10.1016/j.ipm.2021.102694 Search in Google Scholar

[20] M. H. Aghdam, A novel constrained non-negative matrix factorization method based on users and items pairwise relationship for recommender systems, Expert Systems with Applications (2022) 116593.10.1016/j.eswa.2022.116593 Search in Google Scholar

[21] D. D. Lee, H. S. Seung, Algorithms for nonnegative matrix factorization, in: Advances in neural information processing systems, 2001, pp. 556–562. Search in Google Scholar

[22] D. Cai, X. He, J. Han, T. S. Huang, Graph regularized nonnegative matrix factorization for data representation, IEEE transactions on pattern analysis and machine intelligence 33 (8) (2010) 1548–1560.10.1109/TPAMI.2010.23121173440 Search in Google Scholar

[23] H. Liu, Z. Wu, X. Li, D. Cai, T. S. Huang, Constrained nonnegative matrix factorization for image representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (7) (2011) 1299–1311.10.1109/TPAMI.2011.21722064797 Search in Google Scholar

[24] X. Luo, M. Zhou, Y. Xia, Q. Zhu, An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems, IEEE Transactions on Industrial Informatics 10 (2) (2014) 1273–1284.10.1109/TII.2014.2308433 Search in Google Scholar

[25] X. Luo, M. Zhou, S. Li, Z. You, Y. Xia, Q. Zhu, A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method, IEEE transactions on neural networks and learning systems 27 (3) (2015) 579–592.10.1109/TNNLS.2015.241525726011893 Search in Google Scholar

[26] O. Vikas, A. K. Meshram, G. Meena, A. Gupta, Multiple document summarization using principal component analysis incorporating semantic vector space model, in: International Journal of Computational Linguistics & Chinese Language Processing, Volume 13, Number 2, June 2008, 2008, pp. 141–156. Search in Google Scholar

[27] Y. Gong, X. Liu, Generic text summarization using relevance measure and latent semantic analysis, in: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 2001, pp. 19–25.10.1145/383952.383955 Search in Google Scholar

[28] J. Qiang, Y. Li, Y. Yuan, W. Liu, Snapshot ensembles of non-negative matrix factorization for stability of topic modeling, Applied Intelligence 48 (11) (2018) 3963–3975.10.1007/s10489-018-1192-4 Search in Google Scholar

[29] C. Liu, Discriminant analysis and similarity measure, Pattern Recognition 47 (1) (2014) 359–367.10.1016/j.patcog.2013.06.023 Search in Google Scholar

[30] C. C. Aggarwal, C. Zhai, Mining text data, Springer Science & Business Media, 2012.10.1007/978-1-4614-3223-4 Search in Google Scholar

[31] A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society: Series B (Methodological) 39 (1) (1977) 1–22. Search in Google Scholar

[32] G. Erkan, D. R. Radev, Lexrank: Graph-based lexical centrality as salience in text summarization, Journal of artificial intelligence research 22 (2004) 457–479. Search in Google Scholar

[33] A. Ibrahim, T. Elghazaly, M. Gheith, A novel arabic text summarization model based on rhetorical structure theory and vector space model, International Journal of Computational Linguistics and Natural Language Processing 2 (8) (2013) 480–485. Search in Google Scholar

[34] G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval, Vol. 24, Elsevier, 1988, pp. 513–523.10.1016/0306-4573(88)90021-0 Search in Google Scholar

[35] O. Mogren, M. Kågebäack, D. Dubhashi, Extractive summarization by aggregating multiple similarities, in: Proceedings of the International Conference Recent Advances in Natural Language Processing, 2015, pp. 451–457. Search in Google Scholar

[36] C.-Y. Lin, Rouge: A package for automatic evaluation of summaries, in: Text summarization branches out, 2004, pp. 74–81. Search in Google Scholar

[37] R. M. Aliguliyev, A new sentence similarity measure and sentence based extractive technique for automatic text summarization, Expert Systems with Applications 36 (4) (2009) 7764–7772.10.1016/j.eswa.2008.11.022 Search in Google Scholar

[38] X. Wan, Towards a unified approach to simultaneous single-document and multi-document summarizations, in: Proceedings of the 23rd international conference on computational linguistics (Coling 2010), 2010, pp. 1137–1145. Search in Google Scholar

[39] D. Parveen, H.-M. Ramsl, M. Strube, Topical coherence for graph-based extractive summarization, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 1949–1954.10.18653/v1/D15-1226 Search in Google Scholar

[40] R. Mihalcea, P. Tarau, Textrank: Bringing order into text, in: Proceedings of the 2004 conference on empirical methods in natural language processing, 2004, pp. 404–411. Search in Google Scholar

[41] K. Al-Sabahi, Z. Zuping, M. Nadher, A hierarchical structured self-attentive model for extractive document summarization (hssas), IEEE Access 6 (2018) 24205–24212.10.1109/ACCESS.2018.2829199 Search in Google Scholar

[42] J. Cheng, M. Lapata, Neural summarization by extracting sentences and words, arXiv preprint arXiv:1603.07252 (2016).10.18653/v1/P16-1046 Search in Google Scholar

[43] R. Nallapati, B. Zhou, M. Ma, Classify or select: Neural architectures for extractive document summarization, arXiv preprint arXiv:1611.04244 (2016). Search in Google Scholar

[44] K. Yao, L. Zhang, T. Luo, Y. Wu, Deep reinforcement learning for extractive document summarization, Neurocomputing 284 (2018) 52–62.10.1016/j.neucom.2018.01.020 Search in Google Scholar

[45] S. Narayan, S. B. Cohen, M. Lapata, Ranking sentences for extractive summarization with reinforcement learning, arXiv preprint arXiv:1802.08636 (2018).10.18653/v1/N18-1158 Search in Google Scholar

[46] Q. Zhou, N. Yang, F. Wei, S. Huang, M. Zhou, T. Zhao, Neural document summarization by jointly learning to score and select sentences, arXiv preprint arXiv:1807.02305 (2018).10.18653/v1/P18-1061 Search in Google Scholar

[47] Y. Dong, Y. Shen, E. Crawford, H. van Hoof, J. C. K. Cheung, Banditsum: Extractive summarization as a contextual bandit, arXiv preprint arXiv:1809.09672 (2018).10.18653/v1/D18-1409 Search in Google Scholar

eISSN:
2449-6499
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Databases and Data Mining, Artificial Intelligence