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Challenges in Data-to-Document Generation

arXiv 1707.08052 ↗First posted 25 Jul 2017cs.CLcited by 226

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Abstract (arXiv)

Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task. In particular, we introduce a new, large-scale corpus of data records paired with descriptive documents, propose a series of extractive evaluation methods for analyzing performance, and obtain baseline results using current neural generation methods. Experiments show that these models produce fluent text, but fail to convincingly approximate human-generated documents. Moreover, even templated baselines exceed the performance of these neural models on some metrics, though copy- and reconstruction-based extensions lead to noticeable improvements.

Key facts. Cited by 226 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2017.

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Citing papers per year: 2021: 51; 2022: 53; 2023: 43; 2024: 30; 2025: 30; 2026: 19.

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  11. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  12. 2021NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristicscited by 153
38 more
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  10. 2022OpenCQA: Open-ended Question Answering with Chartscited by 60
  11. 2023DINOISER: Diffused Conditional Sequence Learning by Manipulating Noisescited by 50
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  13. 2021Text-to-Table: A New Way of Information Extractioncited by 47
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  15. 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspectioncited by 44
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  18. 2022IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languagescited by 42
  19. 2023SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluationcited by 42
  20. 2021Controlling Hallucinations at Word Level in Data-to-Text Generationcited by 41
  21. 2024Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sportscited by 41
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  23. 2021Automatic Text Evaluation through the Lens of Wasserstein Barycenterscited by 38
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  25. 2022GEMv2: Multilingual NLG Benchmarking in a Single Line of Codecited by 36
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  27. 2021Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learningcited by 34
  28. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  29. 2024Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extractioncited by 33
  30. 2021Open Domain Question Answering with A Unified Knowledge Interfacecited by 32
  31. 2022Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learningcited by 32
  32. 2023Diving Deep into Modes of Fact Hallucinations in Dialogue Systemscited by 32
  33. 2021Does Pretraining for Summarization Require Knowledge Transfer?cited by 30
  34. 2022Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Studycited by 30
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  36. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  37. 2023Elastic Weight Removal for Faithful and Abstractive Dialogue Generationcited by 29
  38. 2022A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generationcited by 28

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