mlsanityData to 14 Oct 2026

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

arXiv 1703.09902 ↗First posted 29 Mar 2017cs.CL cs.AI cs.NEcited by 206

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Abstract (arXiv)

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.

Key facts. Cited by 206 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2017.

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Citing papers per year: 2021: 52; 2022: 45; 2023: 31; 2024: 41; 2025: 25; 2026: 12.

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  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  2. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
  3. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 155
  4. 2021How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVENcited by 124
  5. 2021e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Taskscited by 84
  6. 2022Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodscited by 79
  7. 2023Uncertainty in Natural Language Generation: From Theory to Applicationscited by 61
  8. 2021Plan-then-Generate: Controlled Data-to-Text Generation via Planningcited by 52
  9. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 50
  10. 2023Diffusion Models for Non-autoregressive Text Generation: A Surveycited by 44
  11. 2022IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languagescited by 42
  12. 2021Controlling Hallucinations at Word Level in Data-to-Text Generationcited by 41
38 more
  1. 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 38
  2. 2021Data-to-text Generation with Macro Planningcited by 34
  3. 2021A Study of Automatic Metrics for the Evaluation of Natural Language Explanationscited by 34
  4. 2021StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfercited by 34
  5. 2024AI for social science and social science of AI: A Surveycited by 29
  6. 2021Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNetcited by 27
  7. 2021TellMeWhy: A Dataset for Answering Why-Questions in Narrativescited by 27
  8. 2022Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methodscited by 27
  9. 2024C-RAG: Certified Generation Risks for Retrieval-Augmented Language Modelscited by 27
  10. 2024Facial Affective Behavior Analysis with Instruction Tuningcited by 26
  11. 2021Generate Natural Language Explanations for Recommendationcited by 23
  12. 2021Logic-Consistency Text Generation from Semantic Parsescited by 23
  13. 2023Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designcited by 23
  14. 2021Creativity and Machine Learning: A Surveycited by 21
  15. 2021Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluationcited by 21
  16. 2021Few-Shot Table-to-Text Generation with Prototype Memorycited by 21
  17. 2022Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisementscited by 21
  18. 2022Generative Transformers for Design Concept Generationcited by 21
  19. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  20. 2021Continual Learning for Grounded Instruction Generation by Observing Human Following Behaviorcited by 19
  21. 2021DB-BERT: a Database Tuning Tool that "Reads the Manual"cited by 19
  22. 2022Neural Pipeline for Zero-Shot Data-to-Text Generationcited by 19
  23. 2022Why is constrained neural language generation particularly challenging?cited by 19
  24. 2024Exploring and Benchmarking the Planning Capabilities of Large Language Modelscited by 19
  25. 2021The statistical advantage of automatic NLG metrics at the system levelcited by 18
  26. 2021A Review of Text Style Transfer using Deep Learningcited by 18
  27. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
  28. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 17
  29. 2021Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentationcited by 17
  30. 2021Generative Pre-Trained Transformer for Design Concept Generation: An Explorationcited by 17
  31. 2022Data-to-text Generation with Variational Sequential Planningcited by 17
  32. 2024ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?cited by 17
  33. 2024Evaluating Diversity in Automatic Poetry Generationcited by 17
  34. 2022A survey on text generation using generative adversarial networkscited by 16
  35. 2023A Survey on Event-based News Narrative Extractioncited by 16
  36. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16
  37. 2023AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generationcited by 16
  38. 2024Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generationcited by 16

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