mlsanityData to 7 Oct 2026

Table-to-Text: Describing Table Region with Natural Language

arXiv 1805.11234 ↗First posted 29 May 2018cs.CL cs.AIcited by 41

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Abstract (arXiv)

In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the current state-of-the-art BLEU-4 score from 34.70 to 40.26 and from 33.32 to 39.12, respectively. Furthermore, we introduce an open-domain dataset WIKITABLETEXT including 13,318 explanatory sentences for 4,962 tables. Our model achieves a BLEU-4 score of 38.23, which outperforms template based and language model based approaches.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2018.

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Citing papers per year: 2021: 8; 2022: 3; 2023: 8; 2024: 13; 2025: 9.

41 papers.

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  1. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  2. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  3. 2021FRUIT: Faithfully Reflecting Updated Information in Textcited by 39
  4. 2023Instruct and Extract: Instruction Tuning for On-Demand Information Extractioncited by 35
  5. 2024Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extractioncited by 33
  6. 2022Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learningcited by 32
  7. 2024StructLM: Towards Building Generalist Models for Structured Knowledge Groundingcited by 29
  8. 2021Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric Viewcited by 24
  9. 2021Retrieving Complex Tables with Multi-Granular Graph Representation Learningcited by 24
  10. 2021Sketch and Refine: Towards Faithful and Informative Table-to-Text Generationcited by 21
  11. 2021Visual Understanding of Complex Table Structures from Document Imagescited by 17
  12. 2023A Sequence-to-Sequence&Set Model for Text-to-Table Generationcited by 16
29 more
  1. 2024gTBLS: Generating Tables from Text by Conditional Question Answeringcited by 13
  2. 2025MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmarkcited by 13
  3. 2023Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?cited by 10
  4. 2024Generating Realistic Tabular Data with Large Language Modelscited by 9
  5. 2022AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQLcited by 8
  6. 2024Is This a Bad Table? A Closer Look at the Evaluation of Table Generation from Textcited by 8
  7. 2023TabGenie: A Toolkit for Table-to-Text Generationcited by 7
  8. 2023FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedbackcited by 7
  9. 2024DocTabQA: Answering Questions from Long Documents Using Tablescited by 6
  10. 2025Map&Make: Schema Guided Text to Table Generationcited by 6
  11. 2025HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimizationcited by 5
  12. 2025Large Language Models for Imbalanced Classification: Diversity makes the differencecited by 4
  13. 2022Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generationcited by 3
  14. 2023ReTAG: Reasoning Aware Table to Analytic Text Generationcited by 3
  15. 2023CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Qualitycited by 3
  16. 2024iTBLS: A Dataset of Interactive Conversations Over Tabular Informationcited by 3
  17. 2023Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learningcited by 2
  18. 2024Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentationcited by 2
  19. 2024DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualizationcited by 2
  20. 2024An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generationcited by 2
  21. 2025DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answeringcited by 2
  22. 2025Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Frameworkcited by 2
  23. 2025Evaluating Structured Decoding for Text-to-Table Generation: Evidence from Three Datasetscited by 2
  24. 2021Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Modelscited by 1
  25. 2024CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Modelscited by 1
  26. 2024Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigationcited by 1
  27. 2024LLaSA: Large Language and Structured Data Assistantcited by 0
  28. 2025Factual Inconsistency in Data-to-Text Generation Scales Exponentially with LLM Size: A Statistical Validationcited by 0
  29. 2025Data Transformation Strategies to Remove Heterogeneitycited by 0