Subject Areas : Natural Language Processing
ZahraSadat Hosseini
1
,
SayedGholamHassan Tabatabaei
2
1 -
2 -
Keywords:
Abstract :
References:
[1] S. Jones and M. S. Staveley, "Phrasier: A system for interactive document retrieval using keyphrases", in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pp. 160-167.
[2] Y. Zhang, N. Zincir-Heywood, and E. Milios, "World wide web site summarization", Web intelligence and agent systems: an international journal, Vol. 2, No. 1, 2004, pp. 39-53.
[3] E. Papagiannopoulou and G. Tsoumakas, "A review of keyphrase extraction", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 10, No. 2, 2020, p. e1339.
[4] J. Chen, X. Zhang, Y. Wu, Z. Yan, and Z. Li ", Keyphrase generation with correlation constraints," arXiv preprint arXiv:1808.07185, 2018.
[5] F. Boudin, Y. Gallina, and A. Aizawa, "Keyphrase generation for scientific document retrieval", arXiv preprint arXiv:2106.14726, 2021.
[6] S. Mehrabi, S. A. Mirroshandel, and H. Ahmadifar, "DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System", Journal of Information Systems and Telecommunication (JIST), 2019, p. 204.
[7] K. Barker and N. Cornacchia, "Using noun phrase heads to extract document keyphrases", in Advances in Artificial Intelligence: 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, 2000, pp. 40-52.
[8] I. H. Witten, G. W. Paynter, E. Frank, C. Gutwin, and C. G. Nevill-Manning, "KEA: Practical automatic keyphrase extraction", in Proceedings of the fourth ACM conference on Digital libraries, 1999, pp. 254-255.
[9] S. N. Kim and M.-Y. Kan, "Re-examining automatic keyphrase extraction approaches in scientific articles", in Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (MWE), 2009, pp. 9-16.
[10] C. Zhang, "Automatic keyword extraction from documents using conditional random fields", Journal of Computational Information Systems, 2008, vol. 4, no. 3, pp. 1169-1180.
[11] M. Barla and M. Bieliková, "From ambiguous words to key-concept extraction", in 24th International Workshop on Database and Expert Systems Applications, 2013, pp. 63-67: IEEE.
[12] S. M. H. Khozani and H. Bayat, "Specialization of keyword extraction approach to persian texts", in International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2011, pp. 112-116.
[13] S. R. El-Beltagy and A. Rafea, "KP-Miner: A keyphrase extraction system for English and Arabic documents", Information systems, 2009, Vol. 34, No. 1, pp. 132-144.
[14] S. Rose, D. Engel, N. Cramer, and W. Cowley, "Automatic keyword extraction from individual documents", in Text Mining: Applications and Theory, 2010, pp. 1-20.
[15] R. Campos et al., "Yake! collection-independent automatic keyword extractor", in Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Vol. 40, 2018, pp. 806-810.
[16] R. Mihalcea and P. Tarau, "Textrank: Bringing order into text", in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 2004, pp. 404-411.
[17] X. Wan, and J. Xiao, "Single document keyphrase extraction using neighborhood knowledge", in Proceedings of the 23rd AAAI Conference on Artificial Intelligence, Vol. 8, 2008, pp. 855-860.
[18] A. Bougouin, F. Boudin, and B. Daille, "Topicrank: Graph-based topic ranking for keyphrase extraction", in Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), 2013, pp. 543-551.
[19] T. Tomokiyo and M. Hurst, "A language model approach to keyphrase extraction", in Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment, 2003, pp. 33-40.
[20] Z. Liu, X. Chen, Y. Zheng, and M. Sun, "Automatic keyphrase extraction by bridging vocabulary gap", in Proceedings of the Fifteenth Conference on Computational Natural Language Learning, 2011, pp. 135-144.
[21] E. Doostmohammadi, M. H. Bokaei, and H. Sameti, "Perkey: A persian news corpus for keyphrase extraction and generation", in 2018 9th International Symposium on Telecommunications (IST), 2018, pp. 460-465.
[22] I. Hsu, G. Xiao, N. Premkumar, and P. Nanyun", Discourse-level relation extraction via graph pooling", arXiv preprint arXiv:2101.00124 ,2021.
[23] E. Oro, R. Massimo, and S. Domenico, "Ontology-based information extraction from pdf documents with xonto", International Journal on Artificial Intelligence Tools, Vol. 18, No. 05, 2009, pp. 673-695.
[24] M. Gayathri, and R. J. Kannan, "Ontology based concept extraction and classification of ayurvedic documents", Procedia Computer Science, Vol. 172, 2020, pp. 511-516.
[25] X. Yuan, T. Wang, R. Meng, K. Thaker, P. Brusilovsky, D. He, A. Trischler, "One size does not fit all: Generating and evaluating variable number of keyphrases", arXiv preprint arXiv:1810.05241, 2018.
[26] A. Swaminathan, R. K. Gupta, H. Zhang, D. Mahata, R. Gosangi, and R. R. Shah, "Keyphrase generation for scientific articles using gans (student abstract) ", in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, Vol. 34, No. 10, pp. 13931-13932.
[27] Z. Sun, J. Tang, P. Du, Z.-H. Deng, and J.-Y. Nie, "Divgraphpointer: A graph pointer network for extracting diverse keyphrases", in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, pp. 755-764.
[28] R. Meng, S. Zhao, S. Han, D. He, P. Brusilovsky, and Y. Chi, "Deep keyphrase generation", arXiv preprint arXiv:1704.06879, 2017.
[29] Y. Zhang and W. Xiao, "Keyphrase generation based on deep seq2seq model", IEEE access, Vol. 6, 2018, pp. 46047-46057.
[30] W. Chen, H. P. Chan, P. Li, L. Bing, and I. King, "An integrated approach for keyphrase generation via exploring the power of retrieval and extraction", arXiv preprint arXiv:1904.03454, 2019.
[31] E. Doostmohammadi, M. H. Bokaei, and H. Sameti, "Persian keyphrase generation using sequence-to-sequence models", in 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 2010-2015.
[32] A. Glazkova, and D. Morozov, "Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian", arXiv preprint arXiv:2409.10640, 2024.
[33] A. Glazkova, D. Morozov, and T. Garipov, "Key Algorithms for Keyphrase Generation: Instruction-Based LLMs for Russian Scientific Keyphrases", arXiv preprint arXiv:2410.18040, 2024.
[34] E. Thomas, and S. Vajjala, "Improving Absent Keyphrase Generation with Diversity Heads", in Findings of the Association for Computational Linguistics: NAACL 2024, 2024, pp. 1568-1584.
[35] M. Song, Y. Feng, and L. Jing, "A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase Extraction", arXiv preprint arXiv:2405.16571, 2024.
[36] L. Shen, and X. Le, "An enhanced method on transformer-based model for one2seq keyphrase generation", Electronics, Vol. 12, No. 13, 2023, p. 2968.
[37] N. S. Shirwandkar and S. Kulkarni, "Extractive text summarization using deep learning", in 2018 fourth international conference on computing communication control and automation (ICCUBEA), 2018, pp. 1-5.
[38] M. E. Khademi, M. Fakhredanesh, and S. M. Hoseini, "Farsi conceptual text summarizer: a new model in continuous vector space", Journal of Information Systems and Telecommunication (JIST), Vol. 1, No. 25, 2019, p. 23.
[39] M. Afsharizadeh, H. Ebrahimpour-Komleh, A. Bagheri, and G. Chrupała, "A Survey on Multi-document Summarization and Domain-Oriented Approaches", Journal of Information Systems and Telecommunication (JIST), Vol. 1, No. 37, 2022, p. 68.
[40] M. Allahyari, S. Pouriyeh, M. Assefi, S. Safaei, E.D. Trippe, J.B. Gutierrez, and K. Kochut, "Text summarization techniques: a brief survey", arXiv preprint arXiv:1707.02268, 2017.
[41] S. Gupta, and S. K. Gupta, "Abstractive summarization: An overview of the state of the art", Expert Systems with Applications, Vol. 121, 2019, pp. 49-65.
[42] T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, "Bertscore: Evaluating text generation with bert", arXiv preprint arXiv:1904.09675, 2019.
[43] J. Zhang, Y. Zhao, M. Saleh, and P. Liu, "Pegasus: Pre-training with extracted gap-sentences for abstractive summarization", in International Conference on Machine Learning, 2020, pp. 11328-11339: PMLR.
[44] L. Shen, and X. Le, "An enhanced method on transformer-based model for one2seq keyphrase generation", Electronics, Vol. 12, No. 13 , 2023, p. 2968
[45] N. Datta, "Extractive Text Summarization of Clinical Text Using Deep Learning Models", in 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), 2024, pp. 1-6.
[46] F. Liu, C. Xiong, " A Generative Text Summarization Method Based on mT5 and Large Language Models", in 2023 Eleventh International Conference on Advanced Cloud and Big Data (CBD), 2023, pp. 174-179.
[47] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding", arXiv preprint arXiv:1810.04805, 2018.
[48] M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, "Parsbert: Transformer-based model for persian language understanding", Neural Processing Letters, Vol. 53, 2021, pp. 3831-3847.
[49] K. Cho, B. v. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", arXiv preprint arXiv:1406.1078, 2014.
[50] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need. Advances in neural information processing systems", Advances in neural information processing systems, Vol. 30, 2017.
[51] D. Wang, C. Hansen, L.C. Lima, C. Hansen, M. Maistro, J.G. Simonsen, and C. Lioma, "Multi-Head Self-Attention with Role-Guided Masks", in Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28–April 1, 2021, Proceedings, Part II 43,2021, pp. 432-439.
[52] T. Xiao, Y. Li, J. Zhu, Z. Yu, and T. Liu, "Sharing attention weights for fast transformer", arXiv preprint arXiv:1906.11024, 2019.
[53] S. Yildirim and M. Asgari-Chenaghlu, "Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques", Packt Publishing Ltd, 2021.
[54] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P.J. Liu, "Exploring the limits of transfer learning with a unified text-to-text transformer", The Journal of Machine Learning Research, Vol. 21, No. 1, 2020, pp. 5485-5551.
[55] L. Xue, "mT5: A massively multilingual pre-trained text-to-text transformer", arXiv preprint arXiv:2010.11934, 2020.
[1] S. Jones and M. S. Staveley, "Phrasier: A system for interactive document retrieval using keyphrases", in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pp. 160-167.
[2] Y. Zhang, N. Zincir-Heywood, and E. Milios, "World wide web site summarization", Web intelligence and agent systems: an international journal, Vol. 2, No. 1, 2004, pp. 39-53.
[3] E. Papagiannopoulou and G. Tsoumakas, "A review of keyphrase extraction", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 10, No. 2, 2020, p. e1339.
[4] J. Chen, X. Zhang, Y. Wu, Z. Yan, and Z. Li ", Keyphrase generation with correlation constraints," arXiv preprint arXiv:1808.07185, 2018.
[5] F. Boudin, Y. Gallina, and A. Aizawa, "Keyphrase generation for scientific document retrieval", arXiv preprint arXiv:2106.14726, 2021.
[6] S. Mehrabi, S. A. Mirroshandel, and H. Ahmadifar, "DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System", Journal of Information Systems and Telecommunication (JIST), 2019, p. 204.
[7] K. Barker and N. Cornacchia, "Using noun phrase heads to extract document keyphrases", in Advances in Artificial Intelligence: 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, 2000, pp. 40-52.
[8] I. H. Witten, G. W. Paynter, E. Frank, C. Gutwin, and C. G. Nevill-Manning, "KEA: Practical automatic keyphrase extraction", in Proceedings of the fourth ACM conference on Digital libraries, 1999, pp. 254-255.
[9] S. N. Kim and M.-Y. Kan, "Re-examining automatic keyphrase extraction approaches in scientific articles", in Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (MWE), 2009, pp. 9-16.
[10] C. Zhang, "Automatic keyword extraction from documents using conditional random fields", Journal of Computational Information Systems, 2008, vol. 4, no. 3, pp. 1169-1180.
[11] M. Barla and M. Bieliková, "From ambiguous words to key-concept extraction", in 24th International Workshop on Database and Expert Systems Applications, 2013, pp. 63-67: IEEE.
[12] S. M. H. Khozani and H. Bayat, "Specialization of keyword extraction approach to persian texts", in International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2011, pp. 112-116.
[13] S. R. El-Beltagy and A. Rafea, "KP-Miner: A keyphrase extraction system for English and Arabic documents", Information systems, 2009, Vol. 34, No. 1, pp. 132-144.
[14] S. Rose, D. Engel, N. Cramer, and W. Cowley, "Automatic keyword extraction from individual documents", in Text Mining: Applications and Theory, 2010, pp. 1-20.
[15] R. Campos et al., "Yake! collection-independent automatic keyword extractor", in Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Vol. 40, 2018, pp. 806-810.
[16] R. Mihalcea and P. Tarau, "Textrank: Bringing order into text", in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 2004, pp. 404-411.
[17] X. Wan, and J. Xiao, "Single document keyphrase extraction using neighborhood knowledge", in Proceedings of the 23rd AAAI Conference on Artificial Intelligence, Vol. 8, 2008, pp. 855-860.
[18] A. Bougouin, F. Boudin, and B. Daille, "Topicrank: Graph-based topic ranking for keyphrase extraction", in Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), 2013, pp. 543-551.
[19] T. Tomokiyo and M. Hurst, "A language model approach to keyphrase extraction", in Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment, 2003, pp. 33-40.
[20] Z. Liu, X. Chen, Y. Zheng, and M. Sun, "Automatic keyphrase extraction by bridging vocabulary gap", in Proceedings of the Fifteenth Conference on Computational Natural Language Learning, 2011, pp. 135-144.
[21] E. Doostmohammadi, M. H. Bokaei, and H. Sameti, "Perkey: A persian news corpus for keyphrase extraction and generation", in 2018 9th International Symposium on Telecommunications (IST), 2018, pp. 460-465.
[22] I. Hsu, G. Xiao, N. Premkumar, and P. Nanyun", Discourse-level relation extraction via graph pooling", arXiv preprint arXiv:2101.00124 ,2021.
[23] E. Oro, R. Massimo, and S. Domenico, "Ontology-based information extraction from pdf documents with xonto", International Journal on Artificial Intelligence Tools, Vol. 18, No. 05, 2009, pp. 673-695.
[24] M. Gayathri, and R. J. Kannan, "Ontology based concept extraction and classification of ayurvedic documents", Procedia Computer Science, Vol. 172, 2020, pp. 511-516.
[25] X. Yuan, T. Wang, R. Meng, K. Thaker, P. Brusilovsky, D. He, A. Trischler, "One size does not fit all: Generating and evaluating variable number of keyphrases", arXiv preprint arXiv:1810.05241, 2018.
[26] A. Swaminathan, R. K. Gupta, H. Zhang, D. Mahata, R. Gosangi, and R. R. Shah, "Keyphrase generation for scientific articles using gans (student abstract) ", in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, Vol. 34, No. 10, pp. 13931-13932.
[27] Z. Sun, J. Tang, P. Du, Z.-H. Deng, and J.-Y. Nie, "Divgraphpointer: A graph pointer network for extracting diverse keyphrases", in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, pp. 755-764.
[28] R. Meng, S. Zhao, S. Han, D. He, P. Brusilovsky, and Y. Chi, "Deep keyphrase generation", arXiv preprint arXiv:1704.06879, 2017.
[29] Y. Zhang and W. Xiao, "Keyphrase generation based on deep seq2seq model", IEEE access, Vol. 6, 2018, pp. 46047-46057.
[30] W. Chen, H. P. Chan, P. Li, L. Bing, and I. King, "An integrated approach for keyphrase generation via exploring the power of retrieval and extraction", arXiv preprint arXiv:1904.03454, 2019.
[31] E. Doostmohammadi, M. H. Bokaei, and H. Sameti, "Persian keyphrase generation using sequence-to-sequence models", in 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 2010-2015.
[32] A. Glazkova, and D. Morozov, "Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian", arXiv preprint arXiv:2409.10640, 2024.
[33] A. Glazkova, D. Morozov, and T. Garipov, "Key Algorithms for Keyphrase Generation: Instruction-Based LLMs for Russian Scientific Keyphrases", arXiv preprint arXiv:2410.18040, 2024.
[34] E. Thomas, and S. Vajjala, "Improving Absent Keyphrase Generation with Diversity Heads", in Findings of the Association for Computational Linguistics: NAACL 2024, 2024, pp. 1568-1584.
[35] M. Song, Y. Feng, and L. Jing, "A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase Extraction", arXiv preprint arXiv:2405.16571, 2024.
[36] L. Shen, and X. Le, "An enhanced method on transformer-based model for one2seq keyphrase generation", Electronics, Vol. 12, No. 13, 2023, p. 2968.
[37] N. S. Shirwandkar and S. Kulkarni, "Extractive text summarization using deep learning", in 2018 fourth international conference on computing communication control and automation (ICCUBEA), 2018, pp. 1-5.
[38] M. E. Khademi, M. Fakhredanesh, and S. M. Hoseini, "Farsi conceptual text summarizer: a new model in continuous vector space", Journal of Information Systems and Telecommunication (JIST), Vol. 1, No. 25, 2019, p. 23.
[39] M. Afsharizadeh, H. Ebrahimpour-Komleh, A. Bagheri, and G. Chrupała, "A Survey on Multi-document Summarization and Domain-Oriented Approaches", Journal of Information Systems and Telecommunication (JIST), Vol. 1, No. 37, 2022, p. 68.
[40] M. Allahyari, S. Pouriyeh, M. Assefi, S. Safaei, E.D. Trippe, J.B. Gutierrez, and K. Kochut, "Text summarization techniques: a brief survey", arXiv preprint arXiv:1707.02268, 2017.
[41] S. Gupta, and S. K. Gupta, "Abstractive summarization: An overview of the state of the art", Expert Systems with Applications, Vol. 121, 2019, pp. 49-65.
[42] T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, "Bertscore: Evaluating text generation with bert", arXiv preprint arXiv:1904.09675, 2019.
[43] J. Zhang, Y. Zhao, M. Saleh, and P. Liu, "Pegasus: Pre-training with extracted gap-sentences for abstractive summarization", in International Conference on Machine Learning, 2020, pp. 11328-11339: PMLR.
[44] L. Shen, and X. Le, "An enhanced method on transformer-based model for one2seq keyphrase generation", Electronics, Vol. 12, No. 13 , 2023, p. 2968
[45] N. Datta, "Extractive Text Summarization of Clinical Text Using Deep Learning Models", in 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), 2024, pp. 1-6.
[46] F. Liu, C. Xiong, " A Generative Text Summarization Method Based on mT5 and Large Language Models", in 2023 Eleventh International Conference on Advanced Cloud and Big Data (CBD), 2023, pp. 174-179.
[47] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding", arXiv preprint arXiv:1810.04805, 2018.
[48] M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, "Parsbert: Transformer-based model for persian language understanding", Neural Processing Letters, Vol. 53, 2021, pp. 3831-3847.
[49] K. Cho, B. v. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", arXiv preprint arXiv:1406.1078, 2014.
[50] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need. Advances in neural information processing systems", Advances in neural information processing systems, Vol. 30, 2017.
[51] D. Wang, C. Hansen, L.C. Lima, C. Hansen, M. Maistro, J.G. Simonsen, and C. Lioma, "Multi-Head Self-Attention with Role-Guided Masks", in Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28–April 1, 2021, Proceedings, Part II 43,2021, pp. 432-439.
[52] T. Xiao, Y. Li, J. Zhu, Z. Yu, and T. Liu, "Sharing attention weights for fast transformer", arXiv preprint arXiv:1906.11024, 2019.
[53] S. Yildirim and M. Asgari-Chenaghlu, "Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques", Packt Publishing Ltd, 2021.
[54] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P.J. Liu, "Exploring the limits of transfer learning with a unified text-to-text transformer", The Journal of Machine Learning Research, Vol. 21, No. 1, 2020, pp. 5485-5551.
[55] L. Xue, "mT5: A massively multilingual pre-trained text-to-text transformer", arXiv preprint arXiv:2010.11934, 2020.