{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:17:46Z","timestamp":1785889066746,"version":"3.56.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11281-6","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T20:02:11Z","timestamp":1683921731000},"page":"7709-7742","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Natural Language Generation Using Sequential Models: A Survey"],"prefix":"10.1007","volume":"55","author":[{"given":"Abhishek Kumar","family":"Pandey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjiban Sekhar","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"11281_CR1","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.neucom.2020.12.083","volume":"433","author":"N Dethlefs","year":"2021","unstructured":"Dethlefs N, Schoene A, Cuay\u00e1huitl H (2021) A divide-and-conquer approach to neural natural language generation from structured data. Neurocomputing 433:300\u2013309. https:\/\/doi.org\/10.1016\/j.neucom.2020.12.083","journal-title":"Neurocomputing"},{"key":"11281_CR2","doi-asserted-by":"publisher","first-page":"46206","DOI":"10.1109\/ACCESS.2020.2979115","volume":"8","author":"J Cao","year":"2020","unstructured":"Cao J (2020) Generating natural language descriptions from tables. IEEE Access 8:46206\u201346216. https:\/\/doi.org\/10.1109\/ACCESS.2020.2979115","journal-title":"IEEE Access"},{"key":"11281_CR3","doi-asserted-by":"crossref","unstructured":"Wolf T et al (2020) Transformers: state-of-the-art natural language processing, pp 38\u201345","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"11281_CR4","doi-asserted-by":"crossref","unstructured":"Ruder S (2019) Neural transfer learning for natural language processing","DOI":"10.18653\/v1\/N19-5004"},{"issue":"4","key":"11281_CR5","first-page":"72","volume":"10","author":"M Song","year":"2021","unstructured":"Song M (2021) A study on the predictive analytics powered by the artificial intelligence in the movie industry. Int J Adv smart Converg 10(4):72\u201383","journal-title":"Int J Adv smart Converg"},{"issue":"1","key":"11281_CR6","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/357980.357991","volume":"26","author":"J Weizenbaum","year":"1983","unstructured":"Weizenbaum J (1983) ELIZA\u2014a computer program for the study of natural language communication between man and machine. Commun ACM 26(1):23\u201328. https:\/\/doi.org\/10.1145\/357980.357991","journal-title":"Commun ACM"},{"key":"11281_CR7","doi-asserted-by":"crossref","unstructured":"Colby KM (1976) Artificial paranoia: a computer simulation of paranoid processes, vol 7, no 1","DOI":"10.1016\/S0005-7894(76)80257-2"},{"key":"11281_CR8","unstructured":"Angeli G, Liang P, Klein D (2010) A simple domain-independent probabilistic approach to generation. In: EMNLP 2010\u2014conference on empirical methods in natural language processing, proceedings of the conference, pp 502\u2013512"},{"key":"11281_CR9","unstructured":"Meister C, Pimentel T, Wiher G, Cotterell R (2022) Typical decoding for natural language generation. 2022, [Online]. Available: http:\/\/arxiv.org\/abs\/2202.00666"},{"key":"11281_CR10","unstructured":"McShane M, Leon I (2022) Language generation for broad-coverage, explainable cognitive systems. Adv Cogn Syst X, pp 1\u20136 [Online]. Available: https:\/\/arxiv.org\/abs\/2201.10422v1"},{"key":"11281_CR11","doi-asserted-by":"crossref","unstructured":"Li Z (2022) Text language classification based on dynamic word vector and attention mechanism. In: 2021 international conference on big data analytics for cyber-physical system in smart city, pp 367\u2013375","DOI":"10.1007\/978-981-16-7469-3_42"},{"key":"11281_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108273","author":"GMM Elahi","year":"2022","unstructured":"Elahi GMM, Yang YH (2022) Online learnable keyframe extraction in videos and its application with semantic word vector in action recognition. Pattern Recognit. https:\/\/doi.org\/10.1016\/j.patcog.2021.108273","journal-title":"Pattern Recognit"},{"key":"11281_CR13","doi-asserted-by":"publisher","unstructured":"Pennington J, Socher R, Manning C (2014) {G}lo{V}e: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing ({EMNLP}), pp 1532\u20131543. https:\/\/doi.org\/10.3115\/v1\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"issue":"2","key":"11281_CR14","first-page":"349","volume":"100","author":"EM Dharma","year":"2022","unstructured":"Dharma EM, Gaol FL, Warnars HLHS, Soewito B (2022) The accuracy comparison among Word2Vec, glove, and fasttext towards convolution neural network (CNN) text classification. J Theor Appl Inf Technol 100(2):349\u2013359","journal-title":"J Theor Appl Inf Technol"},{"key":"11281_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/sym13101772","author":"AK Nandanwar","year":"2021","unstructured":"Nandanwar AK, Choudhary J (2021) Semantic features with contextual knowledge-based web page categorization using the glove model and stacked bilstm. Symmetry (Basel). https:\/\/doi.org\/10.3390\/sym13101772","journal-title":"Symmetry (Basel)"},{"key":"11281_CR16","doi-asserted-by":"publisher","unstructured":"Jagfeld G, Jenne S, Vu NT (2018) Sequence-to-sequence models for data-to-text natural language generation: word- vs. character-based processing and output diversity. In: INLG 2018\u201411th International Natural Language Generation Conference, Proceedings, pp 221\u2013232. https:\/\/doi.org\/10.18653\/v1\/w18-6529","DOI":"10.18653\/v1\/w18-6529"},{"key":"11281_CR17","doi-asserted-by":"crossref","unstructured":"Gaur M, Arora M, Prakash V, Kumar Y, Gupta K, Nagrath P (2022) Analyzing natural language essay generator models using long short-term memory neural networks, pp 233\u2013248","DOI":"10.1007\/978-981-16-3071-2_21"},{"key":"11281_CR18","doi-asserted-by":"crossref","unstructured":"Kannan S, Vathsala MK (2022) Mathematical model for application of natural language description in the creation of an animation. In: Emerging research in computing, information, communication and applications, pp 237\u2013251","DOI":"10.1007\/978-981-16-1342-5_19"},{"key":"11281_CR19","doi-asserted-by":"crossref","unstructured":"Shi J, Yang Z, He J, Xu B, Lo D (2022) Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?, no. 1, [Online]. Available: http:\/\/arxiv.org\/abs\/2201.01988","DOI":"10.1109\/SANER53432.2022.00130"},{"key":"11281_CR20","doi-asserted-by":"publisher","first-page":"106770","DOI":"10.1016\/j.infsof.2021.106770","volume":"143","author":"M Li","year":"2022","unstructured":"Li M et al (2022) Automated data function extraction from textual requirements by leveraging semi-supervised CRF and language model. Inf Softw Technol 143:106770. https:\/\/doi.org\/10.1016\/j.infsof.2021.106770","journal-title":"Inf Softw Technol"},{"key":"11281_CR21","doi-asserted-by":"publisher","first-page":"101865","DOI":"10.1016\/j.is.2021.101865","volume":"103","author":"Y Liu","year":"2021","unstructured":"Liu Y, Wang L, Shi T, Li J (2021) Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM. Inf Syst 103:101865. https:\/\/doi.org\/10.1016\/j.is.2021.101865","journal-title":"Inf Syst"},{"issue":"1","key":"11281_CR22","first-page":"205","volume":"25","author":"J Lin","year":"2022","unstructured":"Lin J, Sun G, Beydoun G, Li L (2022) Applying machine translation and language modelling strategies for the recommendation task of micro learning service. Educ Technol Soc 25(1):205\u2013212","journal-title":"Educ Technol Soc"},{"issue":"1","key":"11281_CR23","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1017\/S1351324997001502","volume":"3","author":"E Reiter","year":"1997","unstructured":"Reiter E, Dale R (1997) Building applied natural language generation systems. Nat Lang Eng 3(1):57\u201387. https:\/\/doi.org\/10.1017\/S1351324997001502","journal-title":"Nat Lang Eng"},{"key":"11281_CR24","doi-asserted-by":"crossref","unstructured":"Kunhi LM, Shetty J (2022) Generation of structured query language from natural language using recurrent neural networks. Invent Commun Comput Technol 63\u201373","DOI":"10.1007\/978-981-16-5529-6_6"},{"key":"11281_CR25","doi-asserted-by":"publisher","unstructured":"Zhang X, Lapata M (2014) Chinese poetry generation with recurrent neural networks. In: Proceedings of the 2014 conference on empirical methods in natural language processing ({EMNLP}), pp 670\u2013680. https:\/\/doi.org\/10.3115\/v1\/D14-1074","DOI":"10.3115\/v1\/D14-1074"},{"issue":"c","key":"11281_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1613\/jair.5714","volume":"61","author":"A Gatt","year":"2018","unstructured":"Gatt A, Krahmer E (2018) Survey of the state of the art in natural language generation: core tasks, applications and evaluation. J Artif Intell Res 61(c):1\u201364. https:\/\/doi.org\/10.1613\/jair.5714","journal-title":"J Artif Intell Res"},{"issue":"5","key":"11281_CR27","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/s0092-8674(94)90482-0","volume":"78","author":"VJ Palombella","year":"1994","unstructured":"Palombella VJ, Rando OJ, Goldberg AL, Maniatis T (1994) The ubiquitin-proteasome pathway is required for processing the NF-kappa B1 precursor protein and the activation of NF-kappa B. Cell 78(5):773\u2013785. https:\/\/doi.org\/10.1016\/s0092-8674(94)90482-0","journal-title":"Cell"},{"key":"11281_CR28","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/978-94-009-3645-4_7","volume-title":"Natural language generation: new results in artificial intelligence, psychology and linguistics","author":"WC Mann","year":"1987","unstructured":"Mann WC, Thompson SA (1987) Rhetorical structure theory: description and construction of text structures. In: Kempen G (ed) Natural language generation: new results in artificial intelligence, psychology and linguistics. Springer, Dordrecht, pp 85\u201395"},{"key":"11281_CR29","unstructured":"Santhanam S (2020) Context based text-generation using LSTM networks. [Online]. Available: http:\/\/arxiv.org\/abs\/2005.00048"},{"key":"11281_CR30","unstructured":"Langkilde I (2000) Forest-based statistical sentence generation. [Online]. Available: https:\/\/aclanthology.org\/A00-2023"},{"key":"11281_CR31","doi-asserted-by":"crossref","unstructured":"Yao T et al (2021) Compound figure separation of biomedical images with side loss. In: Deep generative models, and data augmentation, labelling, and imperfections: first workshop, DGM4MICCAI 2021, and first workshop, DALI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, proceedings 1, pp 173\u2013183","DOI":"10.1007\/978-3-030-88210-5_16"},{"key":"11281_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12030473","author":"P Iglesias","year":"2023","unstructured":"Iglesias P, Sicilia M-A, Garc\u00eda-Barriocanal E (2023) Detecting browser drive-by exploits in images using deep learning. Electronics. https:\/\/doi.org\/10.3390\/electronics12030473","journal-title":"Electronics"},{"key":"11281_CR33","doi-asserted-by":"crossref","unstructured":"Zhao M et al (2021) VoxelEmbed: 3D instance segmentation and tracking with voxel embedding based deep learning. In: Machine learning in medical imaging, pp 437\u2013446","DOI":"10.1007\/978-3-030-87589-3_45"},{"key":"11281_CR34","doi-asserted-by":"publisher","first-page":"152","DOI":"10.4028\/www.scientific.net\/JERA.22.152","volume":"22","author":"S Roy","year":"2016","unstructured":"Roy S, Viswanatham VM (2016) Classifying spam emails using artificial intelligent techniques. Int J Eng Res Africa 22:152\u2013161. https:\/\/doi.org\/10.4028\/www.scientific.net\/JERA.22.152","journal-title":"Int J Eng Res Africa"},{"key":"11281_CR35","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1504\/IJCSYSE.2016.079000","volume":"2","author":"S Roy","year":"2016","unstructured":"Roy S, Viswanatham VM, Krishna P (2016) Spam detection using hybrid model of rough set and decorate ensemble. Int J Comput Syst Eng 2:139. https:\/\/doi.org\/10.1504\/IJCSYSE.2016.079000","journal-title":"Int J Comput Syst Eng"},{"key":"11281_CR36","doi-asserted-by":"publisher","first-page":"61008","DOI":"10.1109\/ACCESS.2019.2904337","volume":"7","author":"M Wei","year":"2019","unstructured":"Wei M, Zhang Y (2019) Natural answer generation with attention over instances. IEEE Access 7:61008\u201361017. https:\/\/doi.org\/10.1109\/ACCESS.2019.2904337","journal-title":"IEEE Access"},{"issue":"6","key":"11281_CR37","doi-asserted-by":"publisher","first-page":"44","DOI":"10.5815\/ijitcs.2018.06.05","volume":"10","author":"D Pawade","year":"2018","unstructured":"Pawade D, Sakhapara A, Jain M, Jain N, Gada K (2018) Story scrambler\u2014automatic text generation using word level RNN-LSTM. Int J Inf Technol Comput Sci 10(6):44\u201353. https:\/\/doi.org\/10.5815\/ijitcs.2018.06.05","journal-title":"Int J Inf Technol Comput Sci"},{"issue":"12","key":"11281_CR38","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1109\/TASLP.2018.2842432","volume":"26","author":"S Shen","year":"2018","unstructured":"Shen S, Chen Y, Yang C, Liu Z, Sun M (2018) Zero-shot cross-lingual neural headline generation. IEEE\/ACM Trans Audio Speech Lang Process 26(12):2319\u20132327. https:\/\/doi.org\/10.1109\/TASLP.2018.2842432","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"12","key":"11281_CR39","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TASLP.2020.3009487","volume":"28","author":"Y Chen","year":"2020","unstructured":"Chen Y, Yang C, Liu Z, Sun M (2020) Reinforced zero-shot cross-lingual neural headline generation. IEEE\/ACM Trans Audio Speech Lang Process 28(12):2572\u20132584. https:\/\/doi.org\/10.1109\/TASLP.2020.3009487","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"11281_CR40","doi-asserted-by":"publisher","unstructured":"Abujar S, Masum AKM, Chowdhury SMMH, Hasan M, Hossain SA (2019) Bengali text generation using bi-directional RNN. In: 2019 10th International conference on computing and communication networks technology, ICCCNT 2019, pp 1\u20135. https:\/\/doi.org\/10.1109\/ICCCNT45670.2019.8944784","DOI":"10.1109\/ICCCNT45670.2019.8944784"},{"issue":"2","key":"11281_CR41","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1109\/TASLP.2018.2878381","volume":"27","author":"J Bao","year":"2019","unstructured":"Bao J, Tang D, Duan N, Yan Z, Zhou M, Zhao T (2019) Text generation from tables. IEEE\/ACM Trans Audio Speech Lang Process 27(2):311\u2013320. https:\/\/doi.org\/10.1109\/TASLP.2018.2878381","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"11281_CR42","doi-asserted-by":"publisher","first-page":"106767","DOI":"10.1016\/j.asoc.2020.106767","volume":"97","author":"HC Wang","year":"2020","unstructured":"Wang HC, Hsiao WC, Chang SH (2020) Automatic paper writing based on a RNN and the TextRank algorithm. Appl Soft Comput J 97:106767. https:\/\/doi.org\/10.1016\/j.asoc.2020.106767","journal-title":"Appl Soft Comput J"},{"issue":"8","key":"11281_CR43","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"11281_CR44","doi-asserted-by":"crossref","unstructured":"Roy S, Kaul D, Roy R, Barna C, Mehta S, Misra A (2018) Prediction of customer satisfaction using Naive Bayes, multiclass classifier, K-star and IBK","DOI":"10.1007\/978-3-319-62524-9_12"},{"key":"11281_CR45","doi-asserted-by":"publisher","first-page":"107093","DOI":"10.1016\/j.knosys.2021.107093","volume":"227","author":"Y Ren","year":"2021","unstructured":"Ren Y, Hu W, Wang Z, Zhang X, Wang Y, Wang X (2021) A hybrid deep generative neural model for financial report generation. Knowl Based Syst 227:107093. https:\/\/doi.org\/10.1016\/j.knosys.2021.107093","journal-title":"Knowl Based Syst"},{"key":"11281_CR46","doi-asserted-by":"crossref","unstructured":"Hoogi A, Mishra A, Gimenez F, Dong J, Rubin D (2020) Mammography reports simulation, vol 24, no 9, pp 2711\u20132717","DOI":"10.1109\/JBHI.2020.2980118"},{"issue":"9","key":"11281_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/math8091558","volume":"8","author":"L Xiang","year":"2020","unstructured":"Xiang L, Yang S, Liu Y, Li Q, Zhu C (2020) Novel linguistic steganography based on character-level text generation. Mathematics 8(9):1\u201318. https:\/\/doi.org\/10.3390\/math8091558","journal-title":"Mathematics"},{"key":"11281_CR48","doi-asserted-by":"publisher","unstructured":"Chakraborty S, Banik J, Addhya S, Chatterjee D (2020) Study of dependency on number of LSTM units for character based text generation models. In: 2020 International conference on computer science and engineering and applications, ICCSEA 2020. https:\/\/doi.org\/10.1109\/ICCSEA49143.2020.9132839","DOI":"10.1109\/ICCSEA49143.2020.9132839"},{"key":"11281_CR49","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.procs.2019.05.026","volume":"152","author":"IM Sanzidul","year":"2019","unstructured":"Sanzidul IM, Sadia Sultana SM, Abujar S, Hossain SA (2019) Sequence-to-sequence Bangla sentence generation with LSTM recurrent neural networks. Procedia Comput Sci 152:51\u201358. https:\/\/doi.org\/10.1016\/j.procs.2019.05.026","journal-title":"Procedia Comput Sci"},{"key":"11281_CR50","doi-asserted-by":"crossref","unstructured":"Liu T, Wang K, Sha L, Chang B, Sui Z (2018) Table-to-text generation by structure-aware seq2seq learning. In: 32nd AAAI conference on artificial intelligence, AAAI 2018, pp 4881\u20134888","DOI":"10.1609\/aaai.v32i1.11925"},{"key":"11281_CR51","doi-asserted-by":"crossref","unstructured":"Sha L et al (2018) Order-planning neural text generation from structured data. In: 32nd AAAI conference on artificial intelligence, AAAI 2018, pp 5414\u20135421","DOI":"10.1609\/aaai.v32i1.11947"},{"key":"11281_CR52","doi-asserted-by":"publisher","unstructured":"Fan A, Lewis M, Dauphin Y (2018) Hierarchical neural story generation. In: ACL 2018\u201456th annual meeting of the association for computational linguistics, proceedings conference (long papers), vol 1, pp 889\u2013898. https:\/\/doi.org\/10.18653\/v1\/p18-1082","DOI":"10.18653\/v1\/p18-1082"},{"key":"11281_CR53","doi-asserted-by":"publisher","unstructured":"Li J, Monroe W, A Ritter, Galley M, Gao J, Jurafsky D (2016) Deep reinforcement learning for dialogue generation. IN: EMNLP 2016\u2014conference on empirical methods in natural language processing proceedings, no 4, pp 1192\u20131202. https:\/\/doi.org\/10.18653\/v1\/d16-1127","DOI":"10.18653\/v1\/d16-1127"},{"issue":"6260","key":"11281_CR54","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1126\/science.aac8653","volume":"350","author":"S Bourane","year":"2015","unstructured":"Bourane S et al (2015) Gate control of mechanical itch by a subpopulation of spinal cord interneurons. Science 350(6260):550\u2013554. https:\/\/doi.org\/10.1126\/science.aac8653","journal-title":"Science"},{"key":"11281_CR55","doi-asserted-by":"publisher","first-page":"15844","DOI":"10.1109\/ACCESS.2018.2810849","volume":"6","author":"Q Zheng","year":"2018","unstructured":"Zheng Q, Yang M, Yang J, Zhang Q, Zhang X (2018) Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process. IEEE Access 6:15844\u201315869. https:\/\/doi.org\/10.1109\/ACCESS.2018.2810849","journal-title":"IEEE Access"},{"key":"11281_CR56","doi-asserted-by":"publisher","unstructured":"Zhu J, Li J, Zhu M, Qian L, Zhang M, Zhou G (2020) Modeling graph structure in transformer for better AMR-to-text generation. In: EMNLP-IJCNLP 2019\u20142019 conference on empirical methods natural language processing, 9th international joint conference natural language processing proceedings, vol 1, pp 5459\u20135468. https:\/\/doi.org\/10.18653\/v1\/d19-1548","DOI":"10.18653\/v1\/d19-1548"},{"issue":"1","key":"11281_CR57","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s40998-019-00213-7","volume":"44","author":"R Biswas","year":"2020","unstructured":"Biswas R, Vasan A, Roy SS (2020) Dilated deep neural network for segmentation of retinal blood vessels in fundus images. Iran J Sci Technol Trans Electr Eng 44(1):505\u2013518. https:\/\/doi.org\/10.1007\/s40998-019-00213-7","journal-title":"Iran J Sci Technol Trans Electr Eng"},{"key":"11281_CR58","doi-asserted-by":"publisher","unstructured":"Schmitt M, Sharifzadeh S, Tresp V, Sch\u00fctze H (2020) An unsupervised joint system for text generation from knowledge graphs and semantic parsing. In EMNLP 2020\u20142020 conference on empirical methods natural language processing proceedings, pp 7117\u20137130. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.577","DOI":"10.18653\/v1\/2020.emnlp-main.577"},{"key":"11281_CR59","doi-asserted-by":"publisher","unstructured":"Qader R, Jneid K, Portet F, Labb\u00e9 C (2018) Generation of company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation. In: Proceedings of the 11th international conference on natural language generation, pp 254\u2013263. https:\/\/doi.org\/10.18653\/v1\/W18-6532","DOI":"10.18653\/v1\/W18-6532"},{"issue":"1","key":"11281_CR60","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1162\/COLI_a_00426","volume":"48","author":"D Jin","year":"2022","unstructured":"Jin D, Jin Z, Hu Z, Vechtomova O, Mihalcea R (2022) Deep learning for text style transfer: a survey. Comput Linguist 48(1):155\u2013205. https:\/\/doi.org\/10.1162\/COLI_a_00426","journal-title":"Comput Linguist"},{"key":"11281_CR61","doi-asserted-by":"crossref","unstructured":"Yermakov R, Ag B, Drago N, Ag B, Ziletti A, Ag B (2021) Biomedical data-to-text generation via fine-tuning transformers, pp 364\u2013370","DOI":"10.18653\/v1\/2021.inlg-1.40"},{"issue":"4","key":"11281_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3418052","volume":"38","author":"Y Kim","year":"2020","unstructured":"Kim Y, Jang M, Allan J (2020) Explaining text matching on neural natural language inference. ACM Trans Inf Syst 38(4):1\u201323","journal-title":"ACM Trans Inf Syst"},{"key":"11281_CR63","doi-asserted-by":"publisher","unstructured":"Wang M, Lu S, Zhu D, Lin J, Wang Z (2018) A high-speed and low-complexity architecture for softmax function in deep learning. In: 2018 IEEE Asia Pacific conference on circuits and systems (APCCAS), pp 223\u2013226. https:\/\/doi.org\/10.1109\/APCCAS.2018.8605654","DOI":"10.1109\/APCCAS.2018.8605654"},{"key":"11281_CR64","unstructured":"Bouchard G (2007) Efficient bounds for the softmax function, applications to inference in hybrid models. Nips 1\u20139 [Online]. Available: http:\/\/eprints.pascal-network.org\/archive\/00003498\/"},{"issue":"3","key":"11281_CR65","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1093\/aob\/mcg029","volume":"91","author":"X Yin","year":"2003","unstructured":"Yin X, Goudriaan J, Lantinga EA, Vos J, Spiertz HJ (2003) A flexible sigmoid function of determinate growth. Ann Bot 91(3):361\u2013371. https:\/\/doi.org\/10.1093\/aob\/mcg029","journal-title":"Ann Bot"},{"key":"11281_CR66","unstructured":"Lin C-Y (2004) {ROUGE}: a package for automatic evaluation of summaries. In: Text summarization branches out, pp 74\u201381. Available: https:\/\/aclanthology.org\/W04-1013"},{"key":"11281_CR67","unstructured":"Lin C-Y (2004) Looking for a few good metrics: ROUGE and its evaluation. In: NTCIR Work, pp 1\u20138"},{"key":"11281_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/3411881","volume":"2022","author":"D Yadav","year":"2022","unstructured":"Yadav D et al (2022) Qualitative analysis of text summarization techniques and its applications in health domain. Comput Intell Neurosci 2022:1\u201314. https:\/\/doi.org\/10.1155\/2022\/3411881","journal-title":"Comput Intell Neurosci"},{"key":"11281_CR69","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-022-00863-7","author":"AK Yadav","year":"2022","unstructured":"Yadav AK et al (2022) Extractive text summarization using deep learning approach. Int J Inf Technol. https:\/\/doi.org\/10.1007\/s41870-022-00863-7","journal-title":"Int J Inf Technol"},{"key":"11281_CR70","doi-asserted-by":"publisher","DOI":"10.1002\/int.22821","author":"Y Sun","year":"2022","unstructured":"Sun Y et al (2022) Bidirectional difference locating and semantic consistency reasoning for change captioning. Int J Intell Syst. https:\/\/doi.org\/10.1002\/int.22821","journal-title":"Int J Intell Syst"},{"key":"11281_CR71","doi-asserted-by":"publisher","unstructured":"Papineni K, Roukos S, Ward T, Zhu WJ (2002) BLEU: a method for automatic evaluation of machine translation. https:\/\/doi.org\/10.3115\/1073083.1073135","DOI":"10.3115\/1073083.1073135"},{"key":"11281_CR72","unstructured":"Singh C (2017) Alice in Wonderland Gutenberg. https:\/\/www.kaggle.com\/datasets\/chandan2495\/alice-in-wonderland-gutenbergproject\/metadata"},{"key":"11281_CR73","unstructured":"BG illustrated by A. Browne, Hansel and Gretel (1981). Julia MacRae Books, London, New York"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11281-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11281-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11281-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T21:02:19Z","timestamp":1702414939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11281-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,12]]},"references-count":73,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11281"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11281-6","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,12]]},"assertion":[{"value":"15 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}