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MegaWika: Millions of reports and their sources across 50 diverse languages

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arxiv 2307.07049 v1 pith:TZ46XIU2 submitted 2023-07-13 cs.CL

Samuel Barham , Orion Weller , Michelle Yuan , Kenton Murray , Mahsa Yarmohammadi , Zhengping Jiang , Siddharth Vashishtha , Alexander Martin
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Anqi Liu Aaron Steven White Jordan Boyd-Graber Benjamin Van Durme
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classification cs.CL
keywords generationreportmegawikaapplicationsarticlesautomatedcitationcross-lingual
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To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process this dataset for a myriad of applications, going beyond the initial Wikipedia citation extraction and web scraping of content, including translating non-English articles for cross-lingual applications and providing FrameNet parses for automated semantic analysis. MegaWika is the largest resource for sentence-level report generation and the only report generation dataset that is multilingual. We manually analyze the quality of this resource through a semantically stratified sample. Finally, we provide baseline results and trained models for crucial steps in automated report generation: cross-lingual question answering and citation retrieval.

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Cited by 2 Pith papers

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    cs.CL 2025-09 conditional novelty 6.0 of 10

    mmBERT, a 3T-token encoder-only model pretrained on over 1,800 languages with inverse mask-rate and temperature schedules, substantially outperforms prior multilingual encoders like XLM-R and approaches ModernBERT on English.

  2. A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications

    cs.AI 2025-06 conditional novelty 4.0 of 10

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