# WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

arXiv: https://arxiv.org/abs/2012.14919 · HTML: https://mlsanity.com/paper/2012.14919/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 22 of 22)

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- [MegaWika: Millions of reports and their sources across 50 diverse languages](https://mlsanity.com/paper/2307.07049/) (2023)
- [ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models](https://mlsanity.com/paper/2410.22360/) (2024)
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- [XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages](https://mlsanity.com/paper/2202.00291/) (2022)
- [Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation](https://mlsanity.com/paper/2212.01956/) (2022)
- [MegaWika 2: A More Comprehensive Multilingual Collection of Articles and their Sources](https://mlsanity.com/paper/2508.03828/) (2025)
- [WikiTableEdit: A Benchmark for Table Editing by Natural Language Instruction](https://mlsanity.com/paper/2403.02962/) (2024)
- [eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables](https://mlsanity.com/paper/2502.14820/) (2025)
- [Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework](https://mlsanity.com/paper/2508.12257/) (2025)
- [XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages](https://mlsanity.com/paper/2209.11252/) (2022)
- [Learning from Multiple Sources for Data-to-Text and Text-to-Data](https://mlsanity.com/paper/2302.11269/) (2023)
- [Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generation](https://mlsanity.com/paper/2406.10560/) (2024)
- [Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models](https://mlsanity.com/paper/2410.12878/) (2024)
- [High Recall Data-to-text Generation with Progressive Edit](https://mlsanity.com/paper/2208.04558/) (2022)

## Abstract (arXiv)

Datasets for data-to-text generation typically focus either on multi-domain, single-sentence generation or on single-domain, long-form generation. In this work, we cast generating Wikipedia sections as a data-to-text generation task and create a large-scale dataset, WikiTableT, that pairs Wikipedia sections with their corresponding tabular data and various metadata. WikiTableT contains millions of instances, covering a broad range of topics, as well as a variety of flavors of generation tasks with different levels of flexibility. We benchmark several training and decoding strategies on WikiTableT. Our qualitative analysis shows that the best approaches can generate fluent and high quality texts but they struggle with coherence and factuality, showing the potential for our dataset to inspire future work on long-form generation.