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A Systematic survey on automated text generation tools and techniques: application, evaluation, and challenges

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Abstract

Automatic text generation is the generation of natural language text by machines. Enabling machines to generate readable and coherent text is one of the most vital yet challenging tasks. Traditionally, text generation has been implemented either by using production rules of a predefined grammar or performing statistical analysis of existing human-written texts to predict sequences of words. Recently a paradigm change has emerged in text generation, induced by technological advancements, including deep learning methods and pre-trained transformers. However, many open challenges in text generation need to be addressed, including the generation of fluent, coherent, diverse, controllable, and consistent human-like text. This survey aims to provide a comprehensive overview of current advancements in automated text generation and introduce the topic to researchers by offering pointers and synthesis to pertinent studies. This paper studied the relevant twelve years of articles from 2011 onwards in the field of text generation and observed a total of 146 prime studies relevant to the objective of this survey that has been thoroughly reviewed and discussed. It covers core text generation applications, including text summarization, question–answer generation, story generation, machine translation, dialogue response generation, paraphrase generation, and image/video captioning. The most commonly used datasets for text generation and existing tools with their application domain have also been mentioned. Various text decoding and optimization methods have been provided with their strengths and weaknesses. For evaluating the effectiveness of the generated text, automatic evaluation metrices have been discussed. Finally, the article discusses the main challenges and notable future directions in the field of automated text generation for potential researchers.

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References

  1. Abrishami M, Rashti MJ, Naderan M (2020) Machine Translation Using Improved Attention-based Transformer with Hybrid Input. In: 2020 6th International Conference on Web Research (ICWR). IEEE, pp 52–57

  2. Acharya M, Kafle K, Kanan C (2018) TallyQA: Answering complex counting questions. arXiv. https://doi.org/10.1609/aaai.v33i01.33018076

  3. Agrawal R, Sharma DM (2017) Building an Effective MT System for English-Hindi Using RNN’s. Int J Artif Intell Appl 8:45–58. https://doi.org/10.5121/ijaia.2017.8504

    Article  Google Scholar 

  4. Alloatti F, Di Caro L, Sportelli G (2019) Real Life Application of a Question Answering System Using BERT Language Model. In: Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 250–253

  5. Alomari A, Idris N, Sabri AQM, Alsmadi I (2022) Deep reinforcement and transfer learning for abstractive text summarization: A review. Comput Speech Lang 71:101276. https://doi.org/10.1016/j.csl.2021.101276

  6. Alsaleh A, Althabiti S, Alshammari I, et al (2022) LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an. In: Proceedings ofthe OSACT 2022 Workshop @LREC2022. Eur Lang Res Assoc (ELRA), Marseille, pp 120–125

  7. Ammanabrolu P, Tien E, Cheung W, et al (2019) Guided Neural Language Generation for Automated Storytelling. 46–55.https://doi.org/10.18653/v1/w19-3405

  8. Anderson P, Fernando B, Johnson M, Gould S (2016) SPICE: Semantic propositional image caption evaluation. Lect Notes ComputSci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 9909 LNCS:382–398. https://doi.org/10.1007/978-3-319-46454-1_24

  9. Anderson P, He X, Buehler C, et al (2018) Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 6077–6086.https://doi.org/10.1109/CVPR.2018.00636

  10. Asghar N, Poupart P, Hoey J, et al (2018) Affective neural response generation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 10772 LNCS:154–166. https://doi.org/10.1007/978-3-319-76941-7_12

  11. Bahdanau D, Cho K, Bengio Y (2015) Neural Machine Translation by Jointly Learning to Align and Translate. 3rd Int Conf Learn Represent ICLR 2015 - Conf Track Proc 1–15

  12. Bapna A, Chen MX, Firat O, et al (2020) Training deeper neural machine translation models with transparent attention. Proc 2018 Conf Empir Methods Nat Lang Process EMNLP 2018 3028–3033. https://doi.org/10.18653/v1/d18-1338

  13. Barrull R, Kalita J (2020) Abstractive and mixed summarization for long-single documents. http://arxiv.org/abs/200701918 1–9

  14. Basu S, Ramachandran GS, Keskar NS, Varshney LR (2021) Mirostat: A Neural Text Decoding Algorithm that Directly Controls Perplexity. ArXiv 200714966:1–25

    Google Scholar 

  15. Baumel T, Eyal M, Elhadad M (2018) Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models. arXiv:180107704. arXiv:1801.07704

  16. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 5:157–166. https://doi.org/10.1109/72.279181

    Article  Google Scholar 

  17. Bott S, Saggion H, Figueroa D (2012) A hybrid system for spanish text simplification. 3rd Work Speech Lang Process Assist Technol SLPAT 2012 2012 Conf North Am Chapter Assoc Comput Linguist Hum Lang Technol NAACL-HLT 2012 - Proc 75–84

  18. Bowman SR, Vilnis L, Vinyals O, et al (2016) Generating sentences from a continuous space. CoNLL 2016 - 20th SIGNLL Conf Comput Nat Lang Learn Proc 10–21. https://doi.org/10.18653/v1/k16-1002

  19. Bradbury J, Merity S, Xiong C, Socher R (2016) Quasi-Recurrent Neural Networks. 5th Int Conf Learn Represent 1–11

  20. Brown TB, Mann B, Ryder N, et al (2020) Language Models are Few-Shot Learners. Adv Neural Inf Process Syst

  21. Buck C, Bulian J, Ciaramita M, et al (2018) Ask the Right Questions: Active Question Reformulation with Reinforcement Learning. In: 6th International Conference on Learning Representations, ICLR 2018. Conference Track Proceedings (2018), pp 1–15

  22. Cao Z, Luo C, Li W, Li S (2017) Joint Copying and Restricted Generation for Paraphrase. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI, pp 3152–3158

  23. Cao S, Wang L (2021) Controllable Open-ended Question Generation with A New Question Type Ontology. arXiv 6424–6439. http://arxiv.org/abs/2107.00152

  24. Celikyilmaz A, Clark E, Gao J (2020) Evaluation of Text Generation: A Survey. 1–75.http://arxiv.org/abs/2006.14799

  25. Chen S, Beeferman D, Rosenfeld R (1998) Evaluation metrics for language models. Proc DARPA Broadcast News Transcr Underst Work 275– 280

  26. Chen J, Xiao G, Han X, Chen H (2021) Controllable and Editable Neural Story Plot Generation via Control-and-Edit Transformer. IEEE Access 9:96692–96699. https://doi.org/10.1109/ACCESS.2021.3094263

    Article  Google Scholar 

  27. Chen Y, Xu L, Liu K, et al (2015) Event extraction via dynamic multi-pooling convolutional neural networks. ACL-IJCNLP 2015 - 53rd Annu Meet Assoc Comput Linguist 7th Int Jt Conf Nat Lang Process Asian Fed Nat Lang Process Proc Conf 1:167–176. https://doi.org/10.3115/v1/p15-1017

  28. Cheng J, Lapata M (2016) Neural Summarization by Extracting Sentences and Words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 484–494

  29. Cho K, van Merriënboer B, Gulcehre C, et al (2014) Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. Empir Methods Nat Lang Process (EMNLP), Assoc Comput Linguist 1724–1734. https://doi.org/10.1128/jcm.28.9.2159-.1990

  30. Cho WS, Zhang Y, Rao S, et al (2021) Contrastive Multi-document Question Generation. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 12–30

  31. Chung J, Gulcehre C, Cho K, Bengio Y (2015) Gated Feedback Recurrent Neural Networks. In: 32nd International Conference on Machine Learning, ICML 2015. ICML

  32. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXiv 14123555:1–9

    Google Scholar 

  33. Clark E, Celikyilmaz A, Smith NA (2020) Sentence mover’s similarity: Automatic evaluation for multi-sentence texts. ACL 2019 - 57th Annu Meet Assoc Comput Linguist Proc Conf 2748–2760. https://doi.org/10.18653/v1/p19-1264

  34. Clark E, Ji Y, Smith NA (2018) Neural Text Generation in Stories Using Entity Representations as Context. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 2250–2260

  35. Clinchant S, Jung KW, Nikoulina V (2019) On the use of BERT for Neural Machine Translation. In: Proceedings of the 3rd Workshop on Neural Generation and Translation. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 108–117

  36. Cui Q, Wu S, Liu Q et al (2020) MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation. IEEE Trans Knowl Data Eng 32:317–331. https://doi.org/10.1109/TKDE.2018.2881260

    Article  Google Scholar 

  37. Dai B, Fidler S, Urtasun R, Lin D (2017) Towards Diverse and Natural Image Descriptions via a Conditional GAN. Proc IEEE Int Conf Comput Vis 2017-Octob:2989–2998. https://doi.org/10.1109/ICCV.2017.323

  38. Dauphin YN, Fan A, Auli M, Grangier D (2017) Language modeling with gated convolutional networks. 34th Int Conf Mach Learn ICML 2017 2:1551–1559

  39. Denil M, Demiraj A, Kalchbrenner N et al (2014) Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network. ArXiv 14063830:1–10

    Google Scholar 

  40. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:181004805v2 4171–4186. https://doi.org/10.18653/v1/N19-1423

  41. Dinan E, Roller S, Shuster K, et al (2019) Wizard of Wikipedia: Knowledge-Powered Conversational agents. In: ICLR. pp 1–18

  42. Donahue J, Hendricks LA, Rohrbach M et al (2017) Long-Term Recurrent Convolutional Networks for Visual Recognition and Description. IEEE Trans Pattern Anal Mach Intell 39:677–691. https://doi.org/10.1109/TPAMI.2016.2599174

    Article  Google Scholar 

  43. Dong L, Mallinson J, Reddy S, Lapata M (2017) Learning to paraphrase for question answering. arXiv 875–886

  44. Dong L, Wei F, Zhou M, Xu K (2015) Question Answering over Freebase with Multi-Column Convolutional Neural Networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 260–269

  45. Dozat T (2016) INCORPORATING NESTEROV MOMENTUM INTO ADAM. In: ICLR Workshop. ICLR, pp 2013–2016

  46. Du X, Cardie C (2017) Identifying Where to Focus in Reading Comprehension for Neural Question Generation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 2067–2073

  47. Du X, Shao J, Cardie C (2017) Learning to Ask: Neural Question Generation for Reading Comprehension. arXiv:170500106v1

  48. Duan N, Tang D, Chen P, Zhou M (2017) Question Generation for Question Answering. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 866–874

  49. Duchi JC, Bartlett PL, Wainwright MJ (2012) Randomized smoothing for (parallel) stochastic optimization. In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). IEEE, pp 5442–5444

  50. Dwivedi SK, Singh V (2013) Research and Reviews in Question Answering System. Procedia Technol 10:417–424. https://doi.org/10.1016/j.protcy.2013.12.378

    Article  Google Scholar 

  51. Evans R, Grefenstette E (2018) Learning Explanatory Rules from Noisy Data. J Artif Intell Res 61:1–64. https://doi.org/10.1613/jair.5714

    Article  MathSciNet  MATH  Google Scholar 

  52. Faizan A, Lohmann S (2018) Automatic generation of multiple choice questions from slide content using linked data. ACM Int Conf Proceeding Ser doi 10(1145/3227609):3227656

    Google Scholar 

  53. Fan A, Lewis M, Dauphin Y (2018) Hierarchical neural story generation. ACL 2018 - 56th Annu Meet AssocComput Linguist Proc Conf (Long Pap 1:889–898. https://doi.org/10.18653/v1/p18-1082

  54. Feng B, Liu D, Sun Y (2021) Evolving transformer architecture for neural machine translation. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, New York, NY, USA, pp 273–274

  55. Frome A, Corrado GS, Shelens J et al (2018) DeViSE: A Deep Visual-Semantic Embedding Model Andrea. Phys C Supercond its Appl. https://doi.org/10.1016/0921-4534(95)00110-7

    Article  Google Scholar 

  56. Fung P, Bertero D, Xu P, et al (2014) Empathetic Dialog Systems. In: The International Conference on Language Resources and Evaluation. European Language Resources Association. European Language Resources Association

  57. Gambhir M, Gupta V (2017) Recent automatic text summarization techniques. Artif Intell Rev 47:1–66. https://doi.org/10.1007/s10462-016-9475-9

    Article  Google Scholar 

  58. Gao P, Li H, Li S, et al (2018) Question-Guided Hybrid Convolution for Visual Question Answering. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 11205 LNCS:485–501. https://doi.org/10.1007/978-3-030-01246-5_29

  59. Garbacea C, Mei Q (2020) Neural Language Generation: Formulation, Methods, and Evaluation. http://arxiv.org/abs/200715780

  60. Gardent C, Kow E (2007) A symbolic approach to near-deterministic surface realisation using tree adjoining grammar. In: ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. pp 328–335

  61. Gehring J, Auli M, Grangier D, et al (2017) Convolutional sequence to sequence learning. 34th Int Conf Mach Learn ICML 2017 3:2029–2042

  62. Goldberg Y (2016) A Primer on Neural Network Models for Natural Language Processing. J Artif Intell Res 57:345–420. https://doi.org/10.1613/jair.4992

    Article  MathSciNet  MATH  Google Scholar 

  63. Goyal T, Li JJ, Durrett G (2022) News Summarization and Evaluation in the Era of GPT-3. arXiv. http://arxiv.org/abs/2209.12356

  64. Grechishnikova D (2021) Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Sci Rep 11:321. https://doi.org/10.1038/s41598-020-79682-4

    Article  Google Scholar 

  65. Gu J, Bradbury J, Xiong C, et al (2017) Non-Autoregressive Neural Machine Translation. Proc 2018 Conf Empir Methods Nat Lang Process 479–488

  66. Guan J, Wang Y, Huang M (2019) Story Ending Generation with Incremental Encoding and Commonsense Knowledge. Proc AAAI Conf Artif Intell 33:6473–6480. https://doi.org/10.1609/aaai.v33i01.33016473

    Article  Google Scholar 

  67. Gupta A, Agarwal A, Singh P, Rai P (2018) A deep generative framework for paraphrase generation. 32nd AAAI Conf Artif Intell AAAI 2018 5149–5156

  68. Harrison B, Purdy C, Riedl MO (2021) Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. AAAI, pp 191–197

  69. Harrison V, Walker M (2018) Neural generation of diverse questions using answer focus, contextual and linguistic features. In: Proceedings ofThe 11th International Natural Language Generation Conference. Association for Computational Linguistics, Tilburg, The Netherlands, pp 296–306

  70. Hashimoto TB, Zhang H, Liang P (2019) Unifying human and statistical evaluation for natural language generation. NAACL HLT 2019 - 2019 Conf North Am Chapter Assoc Comput Linguist Hum Lang Technol - Proc Conf 1:1689–1701. https://doi.org/10.18653/v1/n19-1169

  71. He X, Deng L (2017) Deep Learning for VisuaLunDerstanDing Deep Learning for Image-to-Text Generation. IEEE Signal Process Mag 109–116. https://doi.org/10.1109/MSP.2017.2741510

  72. Helcl J, Haddow B, Birch A (2022) Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1780–1790

  73. Hidasi B, Quadrana M, Karatzoglou A, Tikk D (2016) Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems. ACM, New York, NY, USA, pp 241–248

  74. Hochreiter S, Schmidhuber J (21997) Long Short-Term Memory. Neural Comput. https://doi.org/10.17582/journal.pjz/2018.50.6.2199.2207

  75. Holtzman A, Buys J, Du L, et al (2019) The Curious Case of Neural Text Degeneration. CEUR Workshop Proc 2540:

  76. Huang C, Zaïane OR, Trabelsi A, Dziri N (2018) Automatic dialogue generation with expressed emotions. NAACL HLT 2018 - 2018 Conf North Am Chapter AssocComput Linguist Hum Lang Technol - Proc Conf 2:49–54. https://doi.org/10.18653/v1/n18-2008

  77. Iyyer M, Wieting J, Gimpel K, Zettlemoyer L (2018) Adversarial example generation with syntactically controlled paraphrase networks. NAACL HLT 2018 - 2018 Conf North Am Chapter Assoc Comput Linguist Hum Lang Technol - Proc Conf 1:1875–1885. https://doi.org/10.18653/v1/n18-1170

  78. Jain P, Agrawal P, Mishra A, et al (2017) Story Generation from Sequence of Independent Short Descriptions. ArXiv.https://doi.org/10.48550/arXiv.1707.05501

  79. Jha S, Sudhakar A, Singh AK (2018) Learning cross-lingual phonological and orthagraphic adaptations: A case study in improving neural machine translation between low-resource languages. arXiv 1–48. https://doi.org/10.15398/jlm.v7i2.214

  80. Jin J, Fu K, Cui R, et al (2015) Aligning where to see and what to tell: image caption with region-based attention and scene factorization. 1–20

  81. Jozefowicz R, Vinyals O, Schuster M, et al (2016) Exploring the Limits of Language Modeling. arXiv:160202410

  82. Kalchbrenner N, Blunsom P (2013) Recurrent continuous translation models. EMNLP 2013 - 2013 Conf Empir Methods Nat Lang Process Proc Conf 1700–1709

  83. Kamal Eddine M, Shang G, Tixier A, Vazirgiannis M (2022) FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1305–1318

  84. Kannan A, Kurach K, Ravi S, et al (2016) Smart Reply. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, pp 955–964

  85. Karpathy A, Joulin A, Fei-Fei L (2014) Deep Fragment Embeddings for Bidirectional Image Sentence Mapping. 27th International Conference on Neural Information Processing Systems (NIPS’14). MIT Press, Cambridge, MA, USA, pp 1889–1897

    Google Scholar 

  86. Keneshloo Y, Shi T, Ramakrishnan N et al (2020) Deep Reinforcement Learning for Sequence-to-Sequence Models 31:2469–2489

    Google Scholar 

  87. Khamparia A, Pandey B, Tiwari S et al (2020) An Integrated Hybrid CNN–RNN Model for Visual Description and Generation of Captions. Circuits, Syst Signal Process 39:776–788. https://doi.org/10.1007/s00034-019-01306-8

    Article  Google Scholar 

  88. Kim Y, Lee H, Shin J, Jung K (2019) Improving Neural Question Generation Using Answer Separation. Thirty-Third AAAI Conf Artif Intell Improv

  89. Kingma DP, Ba J (2015) Adam: A Method for Stochastic Optimization. In: 3rd International Conference on Learning Representations. ICLR 2015, pp 1–15

  90. Kiros R, Salakhutdinov R, Zemel R (2014) Multimodal neural language models. 31st Int Conf Mach Learn ICML 2014 3:2012–2025

  91. Kiros R, Salakhutdinov R, Zemel RS (2014) Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. ArXiv 14112539:1–13

    Google Scholar 

  92. Kitchenham B, Pearl Brereton O, Budgen D et al (2009) Systematic literature reviews in software engineering - A systematic literature review. Inf Softw Technol 51:7–15. https://doi.org/10.1016/j.infsof.2008.09.009

    Article  Google Scholar 

  93. Knight K, Marcu D (2000) Statistics-Based Summarization - Step One: Sentence Compression. In: Knight2000StatisticsBasedS. American Association for Artificial Intelligence (www.aaai.org), pp 703–710

  94. Kumar A, Irsoy O, Ondruska P, et al (2016) Ask me anything: Dynamic memory networks for natural language processing. 33rd Int Conf Mach Learn ICML 2016 3:2068–2078

  95. Kumar V, Ramakrishnan G, Li YF (2019) Putting the horse before the cart: A generator-evaluator framework for question generation from text. CoNLL 2019 - 23rd ConfComput Nat Lang Learn Proc Conf 812–821. https://doi.org/10.18653/v1/k19-1076

  96. Lavie A, Agarwal A (2005) METEOR: An automatic metric for MT evaluation with high levels of correlation with human judgments. In: Proceedings of the Second Workshop on Statistical Machine Translation

  97. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  98. Lee J, Liang B, Fong H (2021) Restatement and Question Generation for Counsellor Chatbot. In: Proceedings of the 1st Workshop on NLP for Positive Impact. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1–7

  99. Lee J, Yoon W, Kim S, et al (2019) BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 1–7. https://doi.org/10.1093/bioinformatics/btz682

  100. Lelkes AD, Tran VQ, Yu C (2021) Quiz-Style Question Generation for News Stories. In: Proceedings of the Web Conference 2021. ACM, New York, NY, USA, pp 2501–2511

  101. Lemberger P (2020) Deep Learning Models for Automatic Summarization. http://arxiv.org/abs/200511988 1–13

  102. Lewis M, Liu Y, Goyal N, et al (2020) BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. 7871–7880. https://doi.org/10.18653/v1/2020.acl-main.703

  103. Lewis M, Yarats D, Dauphin YN, et al (2017) Deal or no deal? End-to-end learning for negotiation dialogues.EMNLP 2017 - Conf Empir Methods Nat Lang Process Proc 2443–2453. https://doi.org/10.18653/v1/d17-1259

  104. Li J, Galley M, Brockett C, et al (2016) A diversity-promoting objective function for neural conversation models. 2016 Conf North Am Chapter Assoc Comput Linguist Hum Lang Technol NAACL HLT 2016 - Proc Conf 110–119. https://doi.org/10.18653/v1/n16-1014

  105. Li Z, Jiang X, Shang L, Li H (2018) Paraphrase Generation with Deep Reinforcement Learning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 3865–3878

  106. Li B, Lee-urban S, Johnston G, Riedl MO (2013) Story Generation with Crowdsourced Plot Graphs. AAAI, pp 598–604

  107. Li Y, Li K, Ning H, et al (2021) Towards an Online Empathetic Chatbot with Emotion Causes. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, USA, pp 2041–2045

  108. Li J, Luong MT, Jurafsky D (2015) A hierarchical neural Autoencoder for paragraphs and documents. ACL-IJCNLP 2015 - 53rd Annu Meet Assoc Comput Linguist 7th Int Jt Conf Nat Lang Process Asian Fed Nat Lang Process Proc Conf 1:1106–1115. https://doi.org/10.3115/v1/p15-1107

  109. Li J, Monroe W, Jurafsky D (2016) A Simple, Fast Diverse Decoding Algorithm for Neural Generation. ArXiv: 161108562

  110. Li J, Monroe W, Ritter A, et al (2016) Deep reinforcement learning for dialogue generation. EMNLP 2016 - Conf Empir Methods Nat Lang Process Proc 1192–1202. https://doi.org/10.18653/v1/d16-1127

  111. Li J, Monroe W, Shi T, et al (2017) Adversarial learning for neural dialogue generation. EMNLP 2017 - Conf Empir Methods Nat Lang Process Proc 2157–2169.https://doi.org/10.18653/v1/d17-1230

  112. Li S, Tao Z, Li K, Fu Y (2019) Visual to Text: Survey of Image and Video Captioning. IEEE Trans Emerg Top Comput Intell 3:297–312. https://doi.org/10.1109/TETCI.2019.2892755

    Article  Google Scholar 

  113. Liao K, Lebanoff L, Liu F (2018) Abstract Meaning Representation for Multi-Document Summarization. In: International Conference on Computational Linguistics. Santa Fe, New Mexico, USA, pp 1178–1190

  114. Lin C-Y (2004) ROUGE: A Package for Automatic Evaluation of Summaries. In: Text Summarization Branches Out. Association for Computational Linguistics, Barcelona, Spain, pp 74–81

  115. Liu P, Huang C, Mou L (2022) Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization. arXiv 7916–7929. https://doi.org/10.18653/v1/2022.acl-long.545

  116. Liu Y, Lapata M (2019) Text Summarization with Pretrained Encoders. arXiv. http://arxiv.org/abs/1908.08345

  117. Liu X, Lei W, Lv J, Zhou J (2022) Abstract Rule Learning for Paraphrase Generation. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, California, pp 4273–4279

  118. Liu PJ, Saleh M, Pot E, et al (2018) Generating Wikipedia by Summarizing Long Sequences. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. pp 1–18

  119. Liu J, Shen D, Zhang Y, et al (2021) What Makes Good In-Context Examples for GPT-$3$? DeeLIO 2022 - Deep Learn Insid Out 3rd Work Knowl Extr Integr Deep Learn Archit Proc Work 3:100–114. http://arxiv.org/abs/2101.06804

  120. Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26. https://doi.org/10.1016/j.neucom.2016.12.038

    Article  Google Scholar 

  121. Lopyrev K (2015) Generating News Headlines with Recurrent Neural Networks. ArXiv 151201712:1–9

    Google Scholar 

  122. Lu J, Yang J, Batra D, Parikh D (2016) Hierarchical Question-Image Co-Attention for Visual Question Answering. Adv Neural Inf Process Syst 289–297

  123. Lu S, Zhu Y, Zhang W, et al (2018) Neural Text Generation: Past, Present and Beyond. http://arxiv.org/abs/180307133

  124. Luong M-T, Pham H, D. Manning C (2015) Effective Approaches to Attention-based Neural Machine Translation. In: Proceedings ofthe 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, pp 1412–1421

  125. Ma S, Sun X, Li W, et al (2018) Query and output: Generating words by querying distributed word representations for paraphrase generation. NAACL HLT 2018 - 2018 Conf North Am Chapter Assoc Comput Linguist Hum Lang Technol - Proc Conf 1:196–206. https://doi.org/10.18653/v1/n18-1018

  126. Makav B, Kilic V (2019) A New Image Captioning Approach for Visually Impaired People. ELECO 2019 - 11th Int Conf Electr Electron Eng 945–949. https://doi.org/10.23919/ELECO47770.2019.8990630

  127. Mao J, Xu W, Yang Y, et al (2015) Deep captioning with multimodal recurrent neural networks (m-RNN). 3rd Int Conf Learn Represent ICLR 2015 - Conf Track Proc 1090:1–17

  128. Martin LJ, Ammanabrolu P, Wang X, et al (2018) Event representations for automated story generation with deep neural nets. 32nd AAAI Conf Artif Intell AAAI 2018 868–875

  129. Mehta P, Arora G, Majumder P (2018) Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data. Assoc Comput Mach 2–5. https://doi.org/10.48550/arXiv.1802.04675

  130. Michalopoulos G, Chen H, Wong A (2020) Where’s the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data.215–226. https://doi.org/10.18653/v1/2020.clinicalnlp-1.24

  131. Mou L, Song Y, Yan R, et al (2016) Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation. In: COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers. COLING, pp 3349–3358

  132. Mridha MF, Lima AA, Nur K et al (2021) A Survey of Automatic Text Summarization: Progress, Process and Challenges. IEEE Access 9:156043–156070. https://doi.org/10.1109/ACCESS.2021.3129786

    Article  Google Scholar 

  133. Nag D, Das B, Dash PS, et al (2015) From word embeddings to document distances. In: 32nd International Conference on International Conference on Machine Learning. ICML’15, Lille, France, pp 957–966

  134. Nallapati R, Zhai F, Zhou B (2017) SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017. pp 3075–3081

  135. Nallapati R, Zhou B, dos Santos C, et al (2016) Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. In: Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 280–290

  136. 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). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1747–1759

  137. Narayan S, Gardent C (2012) Structure-Driven Lexicalist Generation. 24th International Confer- ence in Computational Linguistics (COLING). Mumbai, India, pp 100–113

    Google Scholar 

  138. Narayan S, Gardent C (2020) Deep Learning Approaches to Text Production. Synth Lect Hum Lang Technol 13:1–199. https://doi.org/10.2200/S00979ED1V01Y201912HLT044

    Article  Google Scholar 

  139. Narayan S, Gardent C, Narayan S, et al (2015) Hybrid Simplification using Deep Semantics and Machine Translation To cite this version : HAL Id : hal-01109581

  140. Nguyen A (2021) Language Model Evaluation in Open-ended Text Generation. arXiv. http://arxiv.org/abs/2108.03578

  141. Niu T, Bansal M (2018) Polite dialogue generation without parallel data. arXiv. https://doi.org/10.1162/tacl_a_00027

  142. PadmaPriya G, Duraiswamy K (2014) AN APPROACH FOR TEXT SUMMARIZATION USING DEEP LEARNING ALGORITHM. J Comput Sci 10:1–9. https://doi.org/10.3844/jcssp.2014.1.9

    Article  Google Scholar 

  143. Papineni K, Roukos S, Ward T, Zhu W-J (2002) BLEU: a Method for Automatic Evaluation of Machine Translation. In: 40th Annual Meeting of the Association for Computational Linguistics (ACL). ACL, pp 311–318

  144. Park HJ, Lee JS, Ko JG (2020) Achieving Real-Time Sign Language Translation Using a Smartphone’s True Depth Images. In: 12th International Conference on Communication Systems & Networks (COMSNETS). IEEE, pp 622–625

  145. Parveen D, Mesgar M, Strube M (2016) Generating coherent summaries of scientific articles using coherence patterns. EMNLP 2016 - Conf Empir Methods Nat Lang Process Proc 772–783. https://doi.org/10.18653/v1/d16-1074

  146. Pascanu R, Mikolov T, Bengio Y (2018) On the difficulty of training recurrent neural networks. Phylogenetic Divers Appl Challenges Biodivers Sci 41–71. https://doi.org/10.1007/978-3-319-93145-6_3

  147. Paulus R, Xiong C, Socher R (2017) A Deep Reinforced Model for Abstractive Summarization. 6th Int Conf Learn Represent ICLR 2018 - Conf Track Proc 1–12

  148. Pauws S, Gatt A, Krahmer E, Reiter E (2019) Making effective use of healthcare data using data-to-text technology. Data Sci Healthc Methodol Appl 119–145. https://doi.org/10.1007/978-3-030-05249-2_4

  149. Pawade D, Sakhapara A, Jain M et al (2018) Story Scrambler - Automatic Text Generation Using Word Level RNN-LSTM. Int J Inf Technol Comput Sci 10:44–53. https://doi.org/10.5815/ijitcs.2018.06.05

    Article  Google Scholar 

  150. Pedersoli M, Lucas T, Schmid C, Verbeek J (2017) Areas of Attention for Image Captioning. Proc IEEE Int Conf Comput Vis 2017-Octob:1251–1259. https://doi.org/10.1109/ICCV.2017.140

  151. Peng N, Ghazvininejad M, May J, Knight K (2018) Towards Controllable Story Generation. In: Proceedings of the First Workshop on Storytelling. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 43–49

  152. Peng B, Yao K (2015) Recurrent Neural Networks with External Memory for Language Understanding. arXiv:150600195v1

  153. Peng D, Zhou M, Liu C, Ai J (2020) Human–machine dialogue modelling with the fusion of word- and sentence-level emotions. Knowledge-Based Syst 192:105319. https://doi.org/10.1016/j.knosys.2019.105319

  154. Portet F, Reiter E, Gatt A et al (2009) Automatic generation of textual summaries from neonatal intensive care data. Artif Intell 173:789–816. https://doi.org/10.1016/j.artint.2008.12.002

    Article  Google Scholar 

  155. Prakash A, Hasan SA, Lee K, et al (2016) Neural paraphrase generation with stacked residual LSTM Networks. COLING 2016 - 26th Int Conf Comput Linguist Proc COLING 2016 Tech Pap 2923–2934

  156. Przybocki M, Peterson K, Bronsart S, Sanders G (2009) The NIST 2008 metrics for machine translation challenge-overview, methodology, metrics, and results. Mach Transl 23:71–103. https://doi.org/10.1007/s10590-009-9065-6

    Article  Google Scholar 

  157. Qi W, Gong Y, Jiao J, et al (2021) BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining

  158. Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12:145–151. https://doi.org/10.1016/S0893-6080(98)00116-6

    Article  Google Scholar 

  159. Qian Q, Huang M, Zhao H, et al (2018) Assigning personality/identity to a chatting machine for coherent conversation generation. Proc Twenty-Seventh Int Jt Conf Artif Intell 4279–4285

  160. Qian L, Qiu L, Zhang W, et al (2019) Exploring Diverse Expressions for Paraphrase Generation. 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). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 3171–3180

  161. Qian L, Zhou H, Bao Y, et al (2021) Glancing Transformer for Non-Autoregressive Neural Machine Translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1993–2003

  162. Radford A, Narasimhan K (2018) Improving Language Understanding by Generative Pre-Training

  163. Raffel C, Shazeer N, Roberts A, et al (2020) Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv:191010683v2 1–67

  164. Rajasekar AA, Garera N (2021) Answer Generation for Questions With Multiple Information Sources in E-Commerce. Proc Flip DS Conf 1:

  165. Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proc 2016 Conf Empir Methods Nat Lang Process 2383–2392.https://doi.org/10.18653/v1/D16-1264

  166. Ranzato M, Chopra S, Auli M, Zaremba W (2016) Sequence Level Training with Recurrent Neural Networks. In: 4th International Conference on Learning Representations, ICLR. ICLR, pp 1–16

  167. Rashkin H, Smith EM, Li M, Boureau YL (2020) Towards empathetic open-domain conversation models: A new benchmark and dataset. ACL 2019 - 57th Annu Meet Assoc Comput Linguist Proc Conf 5370–5381. https://doi.org/10.18653/v1/p19-1534

  168. Reiter E, Dale R (1997) Building applied natural language generation systems. Nat Lang Eng 3:57–87. https://doi.org/10.1017/S1351324997001502

    Article  Google Scholar 

  169. Ren Z, Wang X, Zhang N, et al (2017) Deep reinforcement learning-based image captioning with embedding reward. Proc - 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2017-Janua:1151–1159. https://doi.org/10.1109/CVPR.2017.128

  170. Roemmele M (2016) Writing Stories with Help from Recurrent Neural Networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) Writing. AAAI, pp 4311–4312

  171. Roemmele M, Gordon AS (2015) Interactive Storytelling. Springer International Publishing, Cham

    Google Scholar 

  172. Rush AM, Chopra S, Weston J (2015) A Neural Attention Model for Abstractive Sentence Summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 379–389

  173. Santhanam S, Shaikh S (2019) A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past. Present and Future Directions A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions. https://doi.org/10.5087/dad.DOINUMBER

  174. Saxena SS, Saranya G, Aggarwal D (2020) A Convolutional Recurrent Neural Network ( CRNN ) Based Approach for Text Recognition and Conversion of Text To Speech in Various Indian Languages. Int J Adv Sci Technol 29:2770–2776

    Google Scholar 

  175. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673–2681. https://doi.org/10.1109/78.650093

    Article  Google Scholar 

  176. Scialom T, Hill F (2021) BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation. arXiv 1–20

  177. See A, Liu PJ, Manning CD (2017) Get To The Point: Summarization with Pointer-Generator Networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1073–1083

  178. Serban I V., Sordoni A, Bengio Y, et al (2016) Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. In: 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI Press, pp 3776–3783

  179. Shetty R, Rohrbach M, Hendricks LA, et al (2017) Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training. Proc IEEE Int Conf Comput Vis 2017-Octob:4155–4164. https://doi.org/10.1109/ICCV.2017.445

  180. Song L, Wang Z, Hamza W, et al (2018) Leveraging Context Information for Natural Question Generation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 569–574

  181. Sordoni A, Galley M, Auli M, et al (2015) A Neural Network Approach to Context-Sensitive Generation of Conversational Responses. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 196–205

  182. Sriram A, Jun H, Satheesh S, Coates A (2018) Cold fusion: Training Seq2seq models together with language models. Proc AnnuConf Int Speech Commun Assoc INTERSPEECH 2018-Septe:387–391. https://doi.org/10.21437/Interspeech.2018-1392

  183. Stasaski K, Rathod M, Tu T, et al (2021) Automatically Generating Cause-and-Effect Questions from Passages. Proc 16th Work Innov Use NLP Build Educ Appl BEA 2021 - held conjunction with 16th Conf Eur Chapter Assoc Comput Linguist EACL 2021 158–170

  184. Su Y, Wang Y, Cai D et al (2021) PROTOTYPE-TO-STYLE: Dialogue Generation with Style-Aware Editing on Retrieval Memory. IEEE/ACM Trans Audio Speech Lang Process 29:2152–2161. https://doi.org/10.1109/TASLP.2021.3087948

    Article  Google Scholar 

  185. Subramanian S, Wang T, Yuan X, et al (2018) Neural Models for Key Phrase Extraction and Question Generation. In: Proceedings of the Workshop on Machine Reading for Question Answering. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 78–88

  186. Sun X, Liu J, Lyu Y, et al (2018) Answer-focused and Position-aware Neural Question Generation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 3930–3939

  187. Sundermeyer M, Alkhouli T, Wuebker J, Ney H (2014) Translation Modeling with Bidirectional Recurrent Neural Networks Human Language Technology and Pattern Recognition Group. In: Emnlp2014. ACL, pp 14–25

  188. Sutskever I, Vinyals O, Le QV (2014) Sequence to Sequence Learning with Neural Networks. Adv Neural Inf Process Syst 4:3104–3112

    Google Scholar 

  189. Tambwekar P, Dhuliawala M, Martin LJ, et al (2019) Controllable Neural Story Plot Generation via Reward Shaping. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, California, pp 5982–5988

  190. Tian C, Wang Y, Cheng H, et al (2020) Train Once, and Decode As You Like. In: Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Stroudsburg, PA, USA, pp 280–293

  191. Tian Z, Yan R, Mou L, et al (2017) How to make context more useful? An empirical study on context-Aware neural conversational models. ACL 2017 - 55th Annu Meet Assoc Comput Linguist Proc Conf (Long Pap 2:231–236. https://doi.org/10.18653/v1/P17-2036

  192. Tu Z, Lu Z, Yang L, et al (2016) Modeling coverage for neural machine translation. 54th Annu Meet Assoc Comput Linguist ACL 2016 - Long Pap 1:76–85. https://doi.org/10.18653/v1/p16-1008

  193. Upadhya BA, Udupa S, Kamath SS (2019) Deep Neural Network Models for Question Classification in Community Question-Answering Forums. 2019 10th Int Conf Comput Commun Netw Technol ICCCNT 2019 6–11. https://doi.org/10.1109/ICCCNT45670.2019.8944861

  194. Vasisht S, Tirthani V, Eppa A, et al (2022) Automatic FAQ Generation Using Text-to-Text Transformer Model. 2022 3rd Int Conf Emerg Technol INCET 2022 1–7. https://doi.org/10.1109/INCET54531.2022.9823967

  195. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention Is All You Need. In: NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, arXiv:1706.03762v5. CA, USEA

  196. Vedantam R, Zitnick CL, Parikh D (2015) CIDEr: Consensus-based image description evaluation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 4566–4575

  197. Vijayakumar AK, Cogswell M, Selvaraju RR, et al (2018) Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. pp 1–16

  198. Vijayakumar AK, Cogswell M, Selvaraju RR, et al (2016) Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. pp 7371–7379

  199. Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: A neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3156–3164

  200. Wang Q, Li B, Xiao T, et al (2019) Learning Deep Transformer Models for Machine Translation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1810–1822

  201. Wang C, Yang H, Bartz C, Meinel C (2016) Image captioning with deep bidirectional LSTMs. MM 2016 - Proc 2016 ACM Multimed Conf 988–997. https://doi.org/10.1145/2964284.2964299

  202. Wang P, Yang A, Men R, et al (2022) OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework. arXiv. http://arxiv.org/abs/2202.03052

  203. Wang W, Yang N, Wei F, et al (2017) Gated Self-Matching Networks for Reading Comprehension and Question Answering. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 189–198

  204. Wang T, Yuan X, Trischler A (2017) A Joint Model for Question Answering and Question Generation. arXiv:170601450v1

  205. Welleck S, Kulikov I, Kim J, et al (2020) Consistency of a recurrent language model with respect to incomplete decoding. EMNLP 2020 - 2020 Conf Empir Methods Nat Lang Process Proc Conf 5553–5568.https://doi.org/10.18653/v1/2020.emnlp-main.448

  206. Weston J, Chopra S, Bordes A (2015) Memory Networks. 3rd Int Conf Learn Represent ICLR 2015 - Conf Track Proc 1–15

  207. Wilt C, Thayer J, Ruml W (2010) A comparison of greedy search algorithms. In: Proceedings of the 3rd Annual Symposium on Combinatorial Search, SoCS 2010. SoCS 2010, pp 129–136

  208. Wiseman S, Shieber SM, Rush AM (2018) Learning Neural Templates for Text Generation. Proc 2018 Conf Empir Methods Nat Lang Process EMNLP 2018 3174–3187. https://doi.org/10.18653/v1/d18-1356

  209. Wolf T, Sanh V, Chaumond J, Delangue C (2019) TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents. arXiv

  210. Wołk K, Koržinek D (2017) Comparison and adaptation of automatic evaluation metrics for quality assessment of re-speaking. Comput Sci 18:129–144. https://doi.org/10.7494/csci.2017.18.2.129

    Article  Google Scholar 

  211. Woodsend K, Lapata M (2010) Automatic generation of story highlights. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp 565–574

  212. Wu Y, Hu B (2018) Learning to Extract Coherent Summary via Deep Reinforcement Learning. In: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) Learning. Association for the Advancement of Artificial Intelligence, pp 5602–5609

  213. Wu J, Ouyang L, Ziegler DM, et al (2021) Recursively Summarizing Books with Human Feedback

  214. Wu Y, Schuster M, Chen Z, et al (2016) Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv:160908144 1–23

  215. Xiao Y, Wu L, Guo J, et al (2022) A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond. arXiv 00:1–25. http://arxiv.org/abs/2204.09269

  216. Xie Z (2017) Neural Text Generation: A Practical Guide. http://arxiv.org/abs/180307133 1–21

  217. Xie Y, Le L, Zhou Y, Raghavan VV (2018) Deep Learning for Natural Language Processing. Handb Stat 38:317–328. https://doi.org/10.1016/bs.host.2018.05.001

    Article  MATH  Google Scholar 

  218. Xing C, Wu W, Wu Y, et al (2017) Topic aware neural response generation. 31st AAAI Conf Artif Intell AAAI 2017 3351–3357

  219. Xiong C, Merity S, Socher R (2016) Dynamic memory networks for visual and textual question answering. 33rd Int Conf Mach Learn ICML 2016 5:3574–3583

  220. Xu K, Ba JL, Kiros R, et al (2015) Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. In: International Conference on Machine Learning. JMLR: W&CP

  221. Xu W, Li C, Lee M, Zhang C (2020) Multi-task learning for abstractive text summarization with key information guide network. EURASIP J Adv Signal Process 2020:16. https://doi.org/10.1186/s13634-020-00674-7

    Article  Google Scholar 

  222. Yamada K, Knight K (2001) A syntax-based statistical translation model. 523–530.https://doi.org/10.3115/1073012.1073079

  223. Yan Z, Duan N, Bao J, et al (2016) DocChat: An information retrieval approach for chatbot engines using unstructured documents. 54th Annu Meet Assoc Comput Linguist ACL 2016 - Long Pap 1:516–525. https://doi.org/10.18653/v1/p16-1049

  224. Yang Q, Huo Z, Shen D, et al (2019) An End-to-End Generative Architecture for Paraphrase Generation. 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). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 3130–3140

  225. Yang W, Xie Y, Lin A, et al (2019) End-to-End Open-Domain Question Answering with BERTserini. https://doi.org/10.18653/v1/N19-4013

  226. Yao T, Pan Y, Li Y, Mei T (2017) Incorporating copying mechanism in image captioning for learning novel objects. Proc - 30th IEEE ConfComput Vis Pattern Recognition, CVPR 2017 2017-Janua:5263–5271. https://doi.org/10.1109/CVPR.2017.559

  227. Yao L, Peng N, Weischedel R, et al (2019) Plan-and-Write: Towards Better Automatic Storytelling. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, pp 7378–7385

  228. Yao K, Zweig G, Peng B (2015) Attention with Intention for a Neural Network Conversation Model. ArXiv, abs/151008565 1–7

  229. Yin C, Qian B, Wei J, et al (2019) Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network. In: 2019 IEEE International Conference on Data Mining (ICDM). IEEE, pp 728–737

  230. You Q, Jin H, Wang Z, et al (2016) Image captioning with semantic attention. Proc IEEE ComputSoc Conf Comput Vis Pattern Recognit 2016-Decem:4651–4659. https://doi.org/10.1109/CVPR.2016.503

  231. Yu L, Zhang W, Wang J, Yu Y (2017) SeqGAN : Sequence Generative Adversarial Nets with Policy Gradient. In: 31st AAAI Conference on Artificial Intelligence. AAAI, pp 2852–2858

  232. Yu W, Zhu C, Li Z et al (2022) A Survey of Knowledge-Enhanced Text Generation. ACM Comput Surv 1:1–44. https://doi.org/10.1145/3512467

    Article  Google Scholar 

  233. Yuan X, Wang T, Gulcehre C, et al (2017) Machine comprehension by text-to-text neural question generation. arXiv 15–25. https://doi.org/10.18653/v1/w17-2603

  234. Zeiler MD (2012) ADADELTA: An Adaptive Learning Rate Method. ArXiv: 12125701

  235. Zhang S, Dinan E, Urbanek J, et al (2018) Personalizing dialogue agents: I have a dog, do you have pets too? ACL 2018 - 56th Annu Meet Assoc Comput Linguist Proc Conf (Long Pap 1:2204–2213. https://doi.org/10.18653/v1/p18-1205

  236. Zhang T, Kishore V, Wu F, et al (2020) BERTScore: Evaluating Text Generation with BERT. arXiv:190409675 1–41

  237. Zhang X, Lapata M (2017) Sentence Simplification with Deep Reinforcement Learning. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 584–594

  238. Zhang L, Sung F, Liu F, et al (2017) Actor-Critic Sequence Training for Image Captioning. ArXiv: 170609601

  239. Zhang J, Tan J, Wan X (2018) Towards a Neural Network Approach to Abstractive Multi-Document Summarization. arXiv:180107704

  240. Zhang J, Zhao Y, Saleh M, Liu PJ (2019) PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. 37th IntConf Mach Learn ICML 2020 PartF16814:11265–11276

  241. Zhao Y, Ni X, Ding Y, Ke Q (2018) Paragraph-level neural question generation with maxout pointer and gated self-attention networks. Proc 2018 Conf Empir Methods Nat Lang Process EMNLP 2018 3901–3910. https://doi.org/10.18653/v1/d18-1424

  242. Zhou H, Huang M, Zhang T, et al (2018) Emotional chatting machine: Emotional conversation generation with internal and external memory. 32nd AAAI Conf Artif Intell AAAI 2018 730–738

  243. Zhou H, Huang M, Zhang T, et al (2018) Emotional chatting machine: Emotional conversation generation with internal and external memory. Thirty-Second AAAI Conf Artif Intell 730–738

  244. Zhou X, Wang WY (2018) MOJITALK: Generating Emotional Responses at Scale. In: Proceedings of the 56th Annual Meeting ofthe Association for Computational Linguistics (Long Papers). Assoc Comput Ling. Melbourne, Australia, pp 1128–1137

  245. Zhou Q, Yang N, Wei F, et al (2017) Neural Question Generation from Text: A Preliminary Study. arXiv:170401792v3 [csCL]

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Goyal, R., Kumar, P. & Singh, V.P. A Systematic survey on automated text generation tools and techniques: application, evaluation, and challenges. Multimed Tools Appl 82, 43089–43144 (2023). https://doi.org/10.1007/s11042-023-15224-0

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