Natural Language Generation Using Sequential Models: A Survey
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Compliance with stringent legal standards such as GDPR and CCPA is essential, necessitating purpose limitation and data minimization.
“Compliance with stringent legal standards within applicable jurisdictions, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), is essential, necessitating purpose limitation and data minimization [230], [231], [232].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [231]
Cites
1 retracted
Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain
Yadav, D., Lalit, N., Kaushik, R., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3411881 · Cited by 25 · see citing works
RetractedHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741 · see citing works
ELIZA — a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1983) · Communications of the ACM · doi:10.1145/357980.357991 · Cited by 2,765 · see citing works
Palombella, V. J., Rando, O. J., Goldberg, A. L., & Maniatis, T. (1994) · Cell · doi:10.1016/s0092-8674(94)90482-0 · Cited by 2,097 · see citing works
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1082 · Cited by 1,266 · see citing works
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242 · see citing works
Deep Reinforcement Learning for Dialogue Generation
Li, J., Monroe, W., Ritter, A., Jurafsky, D., Galley, M., & Gao, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1127 · Cited by 1,221 · see citing works
A Flexible Sigmoid Function of Determinate Growth
Yin, X. (2002) · Annals of Botany · doi:10.1093/aob/mcg029 · Cited by 801 · see citing works
Building applied natural language generation systems
Reiter, E., & Dale, R. (1997) · Natural Language Engineering · doi:10.1017/s1351324997001502 · Cited by 653 · see citing works
Transfer Learning in Natural Language Processing
Ruder, S., Peters, M. E., Swayamdipta, S., & Wolf, T. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-5004 · Cited by 648 · see citing works
Learning Explanatory Rules from Noisy Data
Evans, R., & Grefenstette, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5714 · Cited by 426 · see citing works
Chinese Poetry Generation with Recurrent Neural Networks
Zhang, X., & Lapata, M. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1074 · Cited by 298 · see citing works
Gate control of mechanical itch by a subpopulation of spinal cord interneurons
Bourane, S., Duan, B., Koch, S. C., Dalet, A., Britz, O., Garcia-Campmany, L., Kim, E., Cheng, L., Ghosh, A., Ma, Q., et al. (2015) · Science · doi:10.1126/science.aac8653 · Cited by 296 · see citing works
Zheng, Q., Yang, M., Yang, J., Zhang, Q., & Zhang, X. (2018) · IEEE Access · doi:10.1109/access.2018.2810849 · Cited by 257 · see citing works
Table-to-Text Generation by Structure-Aware Seq2seq Learning
Liu, T., Wang, K., Sha, L., Chang, B., & Sui, Z. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11925 · Cited by 242 · see citing works
A High-Speed and Low-Complexity Architecture for Softmax Function in Deep Learning
Wang, M., Lu, S., Zhu, D., Lin, J. T., & Wang, Z. (2018) · IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) · doi:10.1109/apccas.2018.8605654 · Cited by 217 · see citing works
Rhetorical Structure Theory: Description and Construction of Text Structures
Mann, W. C., & Thompson, S. A. (1987) · Natural Language Generation · doi:10.1007/978-94-009-3645-4_7 · Cited by 204 · see citing works
Deep Learning for Text Style Transfer: A Survey
Jin, D., Jin, Z., Hu, Z., Vechtomova, O., & Mihalcea, R. F. (2021) · Computational Linguistics · doi:10.1162/coli_a_00426 · Cited by 192 · see citing works
Artificial paranoia: A computer simulation of paranoid processes
(1976) · Behavior Therapy · doi:10.1016/s0005-7894(76)80257-2 · Cited by 172 · see citing works
Order-Planning Neural Text Generation From Structured Data
Sha, L., Mou, L., Liu, T., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11947 · Cited by 101 · see citing works
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning
Zhao, M., Liu, Q., Jha, A., Deng, R., Yao, T., Mahadevan-Jansen, A. M., Tyska, M. J., Millis, B. A., & Huo, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87589-3_45 · Cited by 95 · see citing works
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Zhu, J., Li, J., Zhu, M., Qian, L., Zhang, M., & Zhou, G. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1548 · Cited by 90 · see citing works
Novel Linguistic Steganography Based on Character-Level Text Generation
Xiang, L., Yang, S., Liu, Y., et al. (2020) · Mathematics · doi:10.3390/math8091558 · Cited by 88 · see citing works
Extractive text summarization using deep learning approach
Yadav, A. K., Singh, A., Dhiman, M., Vineet, Kaundal, R., Verma, A., & Yadav, D. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00863-7 · Cited by 78 · see citing works
Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM
Liu, Y., Wang, L., Shi, T., & Li, J. (2021) · Information Systems · doi:10.1016/j.is.2021.101865 · Cited by 71 · see citing works
Dilated Deep Neural Network for Segmentation of Retinal Blood Vessels in Fundus Images
Biswas, R., Vasan, A., & Roy, S. S. (2019) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-019-00213-7 · Cited by 63 · see citing works
Self-citationStory Scrambler - Automatic Text Generation Using Word Level RNN-LSTM
Pawade, D., M.Sakhapara, A., Jain, M., Jain, N., & Gada, K. (2018) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2018.06.05 · Cited by 60 · see citing works
Sequence-to-sequence Bangla Sentence Generation with LSTM Recurrent Neural Networks
Islam, M. S., Mousumi, S. S. S., Abujar, S., & Hossain, S. A. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.05.026 · Cited by 58 · see citing works
Zero-Shot Cross-Lingual Neural Headline Generation
Yana, A., Shen, S., Chen, Y., Yang, C. H., Liu, Z., & Sun, M. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2842432 · Cited by 56 · see citing works
Study of Dependency on number of LSTM units for Character based Text Generation models
Chakraborty, S., Banik, J., Addhya, S., et al. (2020) · doi:10.1109/iccsea49143.2020.9132839 · Cited by 43 · see citing works
Elahi, G. M. M. E., & Yang, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108273 · Cited by 34 · see citing works
Compound Figure Separation of Biomedical Images with Side Loss
Yao, T., Qu, C., Liu, Q., Deng, R., Tian, Y., Xu, J., Jha, A., Bao, S., Zhao, M., Fogo, A. B., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88210-5_16 · Cited by 33 · see citing works
Nandanwar, A. K., & Choudhary, J. (2021) · Symmetry · doi:10.3390/sym13101772 · Cited by 32 · see citing works
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
Schmitt, M., Sharifzadeh, S., Tresp, V., & Schütze, H. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.577 · Cited by 30 · see citing works
Bengali Text generation Using Bi-directional RNN
Abujar, S., Masum, A. K. M., Chowdhury, S., et al. (2019) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt45670.2019.8944784 · Cited by 29 · see citing works
Automatic paper writing based on a RNN and the TextRank algorithm
Wang, H., Hsiao, W.-C., & Chang, S.-H. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106767 · Cited by 27 · see citing works
Classifying Spam Emails Using Artificial Intelligent Techniques
Roy, S. S., & Viswanatham, V. M. (2016) · International journal of engineering research in Africa · doi:10.4028/www.scientific.net/jera.22.152 · Cited by 26 · see citing works
Self-citationNatural Language Generation Model for Mammography Reports Simulation
Hoogi, A., Mishra, A., Gimenez, F., Dong, J., & Rubin, D. L. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2980118 · Cited by 25 · see citing works
Jagfeld, G., Jenne, S., & Vu, N. T. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6529 · Cited by 19 · see citing works
Bidirectional difference locating and semantic consistency reasoning for change captioning
Sun, Y., Li, L., Yao, T., et al. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22821 · Cited by 19 · see citing works
Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?
Shi, J., Yang, Z., He, J., Xu, B., & Lo, D. (2022) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00130 · Cited by 16 · see citing works
Spam detection using hybrid model of rough set and decorate ensemble
Roy, S. S., Viswanatham, V. M., & Krishna, P. V. (2016) · International Journal of Computational Systems Engineering · doi:10.1504/ijcsyse.2016.079000 · Cited by 15 · see citing works
Self-citationPrediction of Customer Satisfaction Using Naive Bayes, MultiClass Classifier, K-Star and IBK
Roy, S. S., Kaul, D., Roy, R., et al. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-62524-9_12 · Cited by 15 · see citing works
Self-citationQader, R., Jneid, K., Portet, F., et al. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6532 · Cited by 15 · see citing works
Generating Natural Language Descriptions From Tables
Cao, J. (2020) · IEEE Access · doi:10.1109/access.2020.2979115 · Cited by 15 · see citing works
Natural Answer Generation With Attention Over Instances
Wei, M., & Zhang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2904337 · Cited by 13 · see citing works
Bao, J., Tang, D., Duan, N., Yan, Z., Zhou, M., & Zhao, T. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2878381 · Cited by 12 · see citing works
Explaining Text Matching on Neural Natural Language Inference
Kim, Y., Jang, M., & Allan, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3418052 · Cited by 12 · see citing works
A divide-and-conquer approach to neural natural language generation from structured data
Dethlefs, N., Schoene, A. M., & Cuayáhuitl, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.12.083 · Cited by 12 · see citing works
Biomedical Data-to-Text Generation via Fine-Tuning Transformers
Yermakov, R., Drago, N., & Ziletti, A. (2021) · International Conference on Natural Language Generation · doi:10.18653/v1/2021.inlg-1.40 · Cited by 12 · see citing works
Reinforced Zero-Shot Cross-Lingual Neural Headline Generation
Yana, A., Chen, Y., Yang, C. H., Liu, Z., & Sun, M. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3009487 · Cited by 8 · see citing works
Li, M., Shi, L., Wang, Y., et al. (2021) · Information and Software Technology · doi:10.1016/j.infsof.2021.106770 · Cited by 6 · see citing works
Detecting Browser Drive-By Exploits in Images Using Deep Learning
Iglesias, P., Sicilia, M., & García‐Barriocanal, E. (2023) · Electronics · doi:10.3390/electronics12030473 · Cited by 6 · see citing works
A hybrid deep generative neural model for financial report generation
Ren, Y., Hu, W., Wang, Z., Zhang, X., Wang, Y., & Wang, X. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107093 · Cited by 5 · see citing works
Analyzing Natural Language Essay Generator Models Using Long Short-Term Memory Neural Networks
Gaur, M., Arora, M., Prakash, V., Kumar, Y., Gupta, K., & Nagrath, P. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3071-2_21 · Cited by 2 · see citing works
Mathematical Model for Application of Natural Language Description in the Creation of an Animation
Kannan, S., & Vathsala, M. K. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-1342-5_19 · Cited by 2 · see citing works
Text Language Classification Based on Dynamic Word Vector and Attention Mechanism
Li, Z. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-7469-3_42 · Cited by 1 · see citing works
Generation of Structured Query Language from Natural Language Using Recurrent Neural Networks
Kunhi, L. M., & Shetty, J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-5529-6_6 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“NLG encompasses challenges such as topic modeling, discourse planning, concept-to-text generation, style transfer, and controllable text generation [36], [202].”
Background
- Authors
- Pandey, A. K., & Roy, S. S.
- Affiliations
- Vellore Institute of Technology University (IN)
- Corresponding author
- Roy, Sanjiban Sekhar
- Venue
- Neural Processing Letters
- Published
- 2023-05-12
- Volume
- 55
- Issue
- 6
- Pages
- 7709-7742
- ISSN
- 1370-4621 (ISSN-L)
- 1573-773X
- OpenAlex
- W4376271691
- Cited by
- 29
- References
- 61
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 9 · 2023: 2
- FWCI
- 2.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Neural Processing Letters (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- natural language generation, sequence-to-sequence models, long short-term memory, recurrent neural networks, gated recurrent unit, activation functions, grammatical error correction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review