mlsanityData to 7 Oct 2026

Data-to-text Generation with Macro Planning

arXiv 2102.02723 ↗First posted 4 Feb 2021cs.CLcited by 34

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Abstract (arXiv)

Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text which is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events and their interactions; they are learnt from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 33 of its 73 references that are in the map. First posted on arXiv 4 Feb 2021.

Builds on

33 of 73 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
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  8. 2017A Deep Reinforced Model for Abstractive Summarizationcited by 364
  9. 2017The E2E Dataset: New Challenges For End-to-End Generationcited by 252
  10. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
  11. 2017Challenges in Data-to-Document Generationcited by 226
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21 more
  1. 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
  2. 2018Fast Abstractive Summarization with Reinforce-Selected Sentence Rewritingcited by 158
  3. 2019Do NLP Models Know Numbers? Probing Numeracy in Embeddingscited by 157
  4. 2017Controllable Abstractive Summarizationcited by 110
  5. 2018Data-to-Text Generation with Content Selection and Planningcited by 106
  6. 2016Controlling Output Length in Neural Encoder-Decoderscited by 75
  7. 2019Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generationcited by 75
  8. 2019Long and Diverse Text Generation with Planning-based Hierarchical Variational Modelcited by 62
  9. 2019Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generationcited by 62
  10. 2019Neural data-to-text generation: A comparison between pipeline and end-to-end architecturescited by 51
  11. 2019Data-to-text Generation with Entity Modelingcited by 45
  12. 2016Pointing the Unknown Wordscited by 42
  13. 2019Simple and Effective Noisy Channel Modeling for Neural Machine Translationcited by 37
  14. 2015What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignmentcited by 31
  15. 2019A Hierarchical Model for Data-to-Text Generationcited by 20
  16. 2019Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)cited by 18
  17. 2019Learning to Select, Track, and Generate for Data-to-Textcited by 14
  18. 2018Scalable Micro-planned Generation of Discourse from Structured Datacited by 12
  19. 2018Bootstrapping Generators from Noisy Datacited by 11
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  21. 2018Operations Guided Neural Networks for High Fidelity Data-To-Text Generationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 11; 2023: 5; 2024: 11; 2025: 3; 2026: 3.

34 papers.

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  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  2. 2022Conditional Generation with a Question-Answering Blueprintcited by 44
  3. 2022IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languagescited by 42
  4. 2022Automatic Generation of Socratic Subquestions for Teaching Math Word Problemscited by 42
  5. 2022GEMv2: Multilingual NLG Benchmarking in a Single Line of Codecited by 36
  6. 2023Long Text and Multi-Table Summarization: Dataset and Methodcited by 23
  7. 2022Data-to-text Generation with Variational Sequential Planningcited by 17
  8. 2024Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generationcited by 16
  9. 2024LitLLMs, LLMs for Literature Review: Are we there yet?cited by 12
  10. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  11. 2022TaTa: A Multilingual Table-to-Text Dataset for African Languagescited by 10
  12. 2023Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discoverycited by 10
22 more
  1. 2024MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checkingcited by 10
  2. 2024Structsum Generation for Faster Text Comprehensioncited by 9
  3. 2023μPLAN: Summarizing using a Content Plan as Cross-Lingual Bridgecited by 7
  4. 2023Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generationcited by 5
  5. 2022Event Transition Planning for Open-ended Text Generationcited by 4
  6. 2024A Systematic Review of Data-to-Text NLGcited by 4
  7. 2025CoPERLex: Content Planning with Event-based Representations for Legal Case Summarizationcited by 4
  8. 2024Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generationcited by 3
  9. 2024Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentarycited by 3
  10. 2022Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Modelscited by 2
  11. 2023Plan-then-Seam: Towards Efficient Table-to-Text Generationcited by 2
  12. 2024An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generationcited by 2
  13. 2021Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Modelscited by 1
  14. 2022Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraintcited by 1
  15. 2024Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generationcited by 1
  16. 2024Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigationcited by 1
  17. 2025Alternatives To Next Token Prediction In Text Generation -- A Surveycited by 1
  18. 2026Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Modelscited by 1
  19. 2024Self-training from Self-memory in Data-to-text Generationcited by 0
  20. 2025Towards Improving Interpretability of Language Model Generation through a Structured Knowledge Discovery Approachcited by 0
  21. 2026Tree-of-Text: A Tree-based Prompting Framework for Table-to-Text Generation in the Sports Domaincited by 0
  22. 2026LOOMSUM:Weaving Quantitative and Narrative Evidence for Faithful Long Text-Table Summarizationcited by 0