mlsanityData to 14 Oct 2026

WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset

arXiv 2305.05432 ↗First posted 9 May 2023cs.CL cs.CVcited by 8

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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.

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

6 of 9 references.

References · 6

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  3. 2021WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learningcited by 287
  4. 2019Good News, Everyone! Context driven entity-aware captioning for news imagescited by 66
  5. 2022Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipediacited by 10
  6. 2022NewsStories: Illustrating articles with visual summariescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 2.

8 papers.