# WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset

arXiv: https://arxiv.org/abs/2305.05432 · HTML: https://mlsanity.com/paper/2305.05432/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 6 of 6)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [Good News, Everyone! Context driven entity-aware captioning for news images](https://mlsanity.com/paper/1904.01475/) (2019)
- [Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia](https://mlsanity.com/paper/2209.10474/) (2022)
- [NewsStories: Illustrating articles with visual summaries](https://mlsanity.com/paper/2207.13061/) (2022)

## Built on by (most cited first, 8 of 8)

- [Graph4MM: Weaving Multimodal Learning with Structural Information](https://mlsanity.com/paper/2510.16990/) (2025)
- [MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation](https://mlsanity.com/paper/2502.04176/) (2025)
- [MLaGA: Multimodal Large Language and Graph Assistant](https://mlsanity.com/paper/2506.02568/) (2025)
- [Modality-Specialized Synergizers for Interleaved Vision-Language Generalists](https://mlsanity.com/paper/2407.03604/) (2024)
- [WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation](https://mlsanity.com/paper/2503.19065/) (2025)
- [Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection](https://mlsanity.com/paper/2507.18952/) (2025)
- [Region-R1: Reinforcing Query-Side Region Cropping for Multi-Modal Re-Ranking](https://mlsanity.com/paper/2604.05268/) (2026)
- [HAM-RAG: Hierarchy-Aware Multimodal RAG for Structure-Faithful Interleaved Generation](https://mlsanity.com/paper/2608.14032/) (2026)

## Abstract (arXiv)

Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data underused. To study multimodal webpage understanding, we introduce the Wikipedia Webpage 2M (WikiWeb2M) suite; the first to retain the full set of images, text, and structure data available in a page. WikiWeb2M can be used for tasks like page description generation, section summarization, and contextual image captioning.