{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:16:45Z","timestamp":1771258605943,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["19BYY076"],"award-info":[{"award-number":["19BYY076"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MF064"],"award-info":[{"award-number":["ZR2021MF064"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018563","name":"Social Science Planning Project of Shandong Province","doi-asserted-by":"publisher","award":["19BJCJ51"],"award-info":[{"award-number":["19BJCJ51"]}],"id":[{"id":"10.13039\/501100018563","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s11227-023-05630-4","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T16:02:27Z","timestamp":1695398547000},"page":"4972-4995","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gtpsum: guided tensor product framework for abstractive summarization"],"prefix":"10.1007","volume":"80","author":[{"given":"Jingan","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kefeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongli","family":"Pei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenling","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"5630_CR1","doi-asserted-by":"crossref","unstructured":"Narayan S, Cohen SB, Lapata M (2018) Ranking sentences for extractive summarization with reinforcement learning. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp 1747\u20131759","DOI":"10.18653\/v1\/N18-1158"},{"key":"5630_CR2","doi-asserted-by":"crossref","unstructured":"Zhou Q, Yang N, Wei F, Huang S, Zhou M, Zhao T (2018) Neural document summarization by jointly learning to score and select sentences. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 654\u2013663","DOI":"10.18653\/v1\/P18-1061"},{"key":"5630_CR3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.eswa.2019.05.011","volume":"133","author":"X Mao","year":"2019","unstructured":"Mao X, Yang H, Huang S, Liu Y, Li R (2019) Extractive summarization using supervised and unsupervised learning. Expert Syst Appl 133:173\u2013181","journal-title":"Expert Syst Appl"},{"key":"5630_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112958","volume":"143","author":"M Mohd","year":"2020","unstructured":"Mohd M, Jan R, Shah M (2020) Text document summarization using word embedding. Expert Syst Appl 143:112958","journal-title":"Expert Syst Appl"},{"key":"5630_CR5","doi-asserted-by":"crossref","unstructured":"Cai T, Shen M, Peng H, Jiang L, Dai Q (2019) Improving transformer with sequential context representations for abstractive text summarization. In: Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9\u201314, 2019, Proceedings, Part I, pp 512\u2013524. Springer","DOI":"10.1007\/978-3-030-32233-5_40"},{"key":"5630_CR6","doi-asserted-by":"crossref","unstructured":"Miao W, Zhang G, Bai Y, Cai D (2019) Improving accuracy of key information acquisition for social media text summarization. In: 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), pp 408\u2013415. IEEE","DOI":"10.1109\/IUCC\/DSCI\/SmartCNS.2019.00094"},{"key":"5630_CR7","doi-asserted-by":"publisher","unstructured":"Zhu C, Hinthorn W. Xu R, Zeng Q, Zeng M, Huang X, Jiang M (2021) Enhancing factual consistency of abstractive summarization. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 718\u2013733. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.58. https:\/\/aclanthology.org\/2021.naacl-main.58","DOI":"10.18653\/v1\/2021.naacl-main.58"},{"key":"5630_CR8","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/978-981-13-5934-7_31","volume":"904","author":"A Mahajani","year":"2019","unstructured":"Mahajani A, Pandya V, Maria I, Sharma D (2019) A comprehensive survey on extractive and abstractive techniques for text summarization. Ambient Commun Comput Syst 904:339\u2013351","journal-title":"Ambient Commun Comput Syst"},{"key":"5630_CR9","doi-asserted-by":"crossref","unstructured":"Huang L, Wu L, Wang L (2020) Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 5094\u20135107","DOI":"10.18653\/v1\/2020.acl-main.457"},{"key":"5630_CR10","first-page":"619","volume":"1","author":"IK Bhat","year":"2018","unstructured":"Bhat IK, Mohd M, Hashmy R (2018) Sumitup: a hybrid single-document text summarizer. Soft Comput Theor Appl 1:619\u2013634","journal-title":"Soft Comput Theor Appl"},{"key":"5630_CR11","doi-asserted-by":"crossref","unstructured":"Wang S, Zhao X, Li B, Ge B, Tang D (2017) Integrating extractive and abstractive models for long text summarization. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp 305\u2013312 . IEEE","DOI":"10.1109\/BigDataCongress.2017.46"},{"key":"5630_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113679","volume":"165","author":"WS El-Kassas","year":"2021","unstructured":"El-Kassas WS, Salama CR, Rafea AA, Mohamed HK (2021) Automatic text summarization: a comprehensive survey. Expert Syst Appl 165:113679","journal-title":"Expert Syst Appl"},{"key":"5630_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2021.101276","volume":"71","author":"A Alomari","year":"2022","unstructured":"Alomari A, Idris N, Sabri AQM, Alsmadi I (2022) Deep reinforcement and transfer learning for abstractive text summarization: A review. Comput Speech Language 71:101276","journal-title":"Comput Speech Language"},{"key":"5630_CR14","doi-asserted-by":"crossref","unstructured":"Kry\u015bci\u0144ski W, McCann B, Xiong C, Socher R (2019) Evaluating the factual consistency of abstractive text summarization. arXiv preprint arXiv:1910.12840","DOI":"10.18653\/v1\/2020.emnlp-main.750"},{"key":"5630_CR15","doi-asserted-by":"publisher","unstructured":"Wang K, Quan X, Wang R (2019) BiSET: Bi-directional selective encoding with template for abstractive summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 2153\u20132162. Association for Computational Linguistics, Florence, Italy https:\/\/doi.org\/10.18653\/v1\/P19-1207. https:\/\/aclanthology.org\/P19-1207","DOI":"10.18653\/v1\/P19-1207"},{"key":"5630_CR16","doi-asserted-by":"crossref","unstructured":"Li H, Zhu J, Zhang J, Zong C, He X (2020) Keywords-guided abstractive sentence summarization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 8196\u20138203","DOI":"10.1609\/aaai.v34i05.6333"},{"key":"5630_CR17","doi-asserted-by":"crossref","unstructured":"Wang F, Song K, Zhang H, Jin L, Cho S, Yao W, Wang X, Chen M, Yu D (2022) Salience allocation as guidance for abstractive summarization. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 6094\u20136106. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates . https:\/\/aclanthology.org\/2022.emnlp-main.409","DOI":"10.18653\/v1\/2022.emnlp-main.409"},{"key":"5630_CR18","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.ins.2021.11.081","volume":"586","author":"J Zeng","year":"2022","unstructured":"Zeng J, Liu T, Jia W, Zhou J (2022) Relation construction for aspect-level sentiment classification. Inf Sci 586:209\u2013223","journal-title":"Inf Sci"},{"key":"5630_CR19","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.10.091","volume":"471","author":"L Xiao","year":"2022","unstructured":"Xiao L, Xue Y, Wang H, Hu X, Gu D, Zhu Y (2022) Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks. Neurocomputing 471:48\u201359","journal-title":"Neurocomputing"},{"key":"5630_CR20","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neucom.2021.12.084","volume":"478","author":"M Xu","year":"2022","unstructured":"Xu M, Zeng B, Yang H, Chi J, Chen J, Liu H (2022) Combining dynamic local context focus and dependency cluster attention for aspect-level sentiment classification. Neurocomputing 478:49\u201369","journal-title":"Neurocomputing"},{"key":"5630_CR21","doi-asserted-by":"crossref","unstructured":"Liu Y, Lapata M (2019) Text summarization with pretrained encoders. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 3730\u20133740","DOI":"10.18653\/v1\/D19-1387"},{"key":"5630_CR22","doi-asserted-by":"crossref","unstructured":"Zhong M, Liu P, Chen Y, Wang D, Qiu X, Huang X-J (2020) Extractive summarization as text matching. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 6197\u20136208","DOI":"10.18653\/v1\/2020.acl-main.552"},{"key":"5630_CR23","unstructured":"Schlag I, Smolensky P, Fernandez R, Jojic N, Schmidhuber J, Gao J (2019) Enhancing the transformer with explicit relational encoding for math problem solving. arXiv preprint . arXiv:1910.06611"},{"key":"5630_CR24","doi-asserted-by":"publisher","unstructured":"Jiang Y, Celikyilmaz A, Smolensky P, Soulos P, Rao S, Palangi H, Fernandez R, Smith C, Bansal M, Gao, J (2021) Enriching transformers with structured tensor-product representations for abstractive summarization. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4780\u20134793. Association for Computational Linguistics, Online . https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.381. https:\/\/aclanthology.org\/2021.naacl-main.381","DOI":"10.18653\/v1\/2021.naacl-main.381"},{"key":"5630_CR25","unstructured":"Hermann KM, Kocisky T, Grefenstette E, Espeholt L, Kay W, Suleyman M, Blunsom P (2015) Teaching machines to read and comprehend. Advances in neural information processing systems. 28"},{"key":"5630_CR26","doi-asserted-by":"crossref","unstructured":"Nallapati R, Zhou B, dos Santos C, Gul\u00e7ehre, \u00c7, Xiang B (2016) Abstractive text summarization using sequence-to-sequence rnns and beyond. In: Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, pp 280\u2013290","DOI":"10.18653\/v1\/K16-1028"},{"key":"5630_CR27","unstructured":"Koupaee M, Wang WY (2018) Wikihow: A large scale text summarization dataset. arXiv preprint. arXiv:1810.09305"},{"key":"5630_CR28","unstructured":"Kim B, Kim H, Kim G (2019) Abstractive summarization of reddit posts with multi-level memory networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 2519\u20132531"},{"key":"5630_CR29","doi-asserted-by":"crossref","unstructured":"Cohan A, Dernoncourt F, Kim DS, Bui T, Kim S, Chang W, Goharian N (2018) A discourse-aware attention model for abstractive summarization of long documents. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp 615\u2013621","DOI":"10.18653\/v1\/N18-2097"},{"key":"5630_CR30","unstructured":"Lin C-Y (2004) Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp 74\u201381"},{"key":"5630_CR31","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang, M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"5630_CR32","unstructured":"Zhang, J, Zhao Y, Saleh M, Liu P (2020) Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In: International Conference on Machine Learning, pp. 11328\u201311339 . PMLR"},{"key":"5630_CR33","doi-asserted-by":"publisher","unstructured":"Li C, Xu W, Li S, Gao S (2018) Guiding generation for abstractive text summarization based on key information guide network. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp 55\u201360. Association for Computational Linguistics, New Orleans, Louisiana. https:\/\/doi.org\/10.18653\/v1\/N18-2009. https:\/\/aclanthology.org\/N18-2009","DOI":"10.18653\/v1\/N18-2009"},{"key":"5630_CR34","doi-asserted-by":"crossref","unstructured":"Zhu C, Hinthorn W, Xu R, Zeng Q, Zeng M, Huang X, Jiang M (2020) Boosting factual correctness of abstractive summarization with knowledge graph. arXiv preprint. arXiv:2003.08612","DOI":"10.18653\/v1\/2021.naacl-main.58"},{"key":"5630_CR35","doi-asserted-by":"publisher","unstructured":"Cao Z, Li W, Li S, Wei F (2018) Retrieve, rerank and rewrite: Soft template based neural summarization. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 152\u2013161. Association for Computational Linguistics, Melbourne, Australia. https:\/\/doi.org\/10.18653\/v1\/P18-1015. https:\/\/aclanthology.org\/P18-1015","DOI":"10.18653\/v1\/P18-1015"},{"key":"5630_CR36","unstructured":"Saito I, Nishida K, Nishida K, Tomita J (2020) Abstractive summarization with combination of pre-trained sequence-to-sequence and saliency models. arXiv preprint. arXiv:2003.13028"},{"key":"5630_CR37","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1162\/tacl_a_00005","volume":"6","author":"Y Liu","year":"2018","unstructured":"Liu Y, Lapata M (2018) Learning structured text representations. Trans Assoc Computat Linguis 6:63\u201375. https:\/\/doi.org\/10.1162\/tacl_a_00005","journal-title":"Trans Assoc Computat Linguis"},{"key":"5630_CR38","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"key":"5630_CR39","doi-asserted-by":"publisher","unstructured":"Dou Z-Y, Liu P, Hayashi H, Jiang Z, Neubig G (2021) GSum: A general framework for guided neural abstractive summarization. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4830\u20134842. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.384. https:\/\/aclanthology.org\/2021.naacl-main.384","DOI":"10.18653\/v1\/2021.naacl-main.384"},{"key":"5630_CR40","doi-asserted-by":"crossref","unstructured":"Palangi H, Smolensky P, He X, Deng L (2018) Question-answering with grammatically-interpretable representations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32","DOI":"10.1609\/aaai.v32i1.12004"},{"key":"5630_CR41","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint. arXiv:1412.6980"},{"key":"5630_CR42","doi-asserted-by":"publisher","unstructured":"Ruan Q, Ostendorff M, Rehm G (2022) HiStruct+: Improving extractive text summarization with hierarchical structure information. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 1292\u20131308. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.102. https:\/\/aclanthology.org\/2022.findings-acl.102","DOI":"10.18653\/v1\/2022.findings-acl.102"},{"key":"5630_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107235","volume":"227","author":"HP Chan","year":"2021","unstructured":"Chan HP, King I (2021) A condense-then-select strategy for text summarization. Knowledge-Based Systems 227:107235","journal-title":"Knowledge-Based Systems"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05630-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05630-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05630-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T10:17:18Z","timestamp":1707905838000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05630-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["5630"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05630-4","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,22]]},"assertion":[{"value":"25 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This term is not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}