mlsanityData to 7 Oct 2026

Innovations in Neural Data-to-text Generation: A Survey

arXiv 2207.12571 ↗First posted 25 Jul 2022cs.CLcited by 10

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Abstract (arXiv)

The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws boundaries separating DTG from the rest of the natural language generation (NLG) landscape, encompassing an up-to-date synthesis of the literature, and highlighting the stages of technological adoption from within and outside the greater NLG umbrella. With this holistic view, we highlight promising avenues for DTG research that not only focus on the design of linguistically capable systems but also systems that exhibit fairness and accountability.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 187 of its 382 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

187 of 382 references.

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50 most cited of 187 All 187 (JSON)

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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 2.

10 papers.

  1. 2023GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Taskscited by 14
  2. 2023Towards Verifiable Text Generation with Symbolic Referencescited by 13
  3. 2023TabGenie: A Toolkit for Table-to-Text Generationcited by 7
  4. 2025LLMs as Span Annotators: A Comparative Study of LLMs and Humanscited by 7
  5. 2023Learning Non-linguistic Skills without Sacrificing Linguistic Proficiencycited by 5
  6. 2022Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmeticcited by 4
  7. 2024A Systematic Review of Data-to-Text NLGcited by 4
  8. 2024Laying Anchors: Semantically Priming Numerals in Language Modelingcited by 1
  9. 2024Information Guided Regularization for Fine-tuning Language Modelscited by 1
  10. 2025Natural Language Generationcited by 0