@inproceedings{martin-etal-2026-wikivideo,
    title = "{W}iki{V}ideo: Article Generation from Multiple Videos",
    author = "Martin, Alexander  and
      Kriz, Reno  and
      Walden, William Gantt  and
      Sanders, Kate  and
      Recknor, Hannah  and
      Yang, Eugene  and
      Ferraro, Francis  and
      Van Durme, Benjamin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.3/",
    pages = "45--70",
    ISBN = "979-8-89176-395-1",
    abstract = "We introduce the task of grounded article generation with the goal of creating a Wikipedia-style article from multiple diverse videos about real-world events{---}from natural disasters to political elections{---}where all the information in the article is supported by video evidence. Videos are intuitive sources for retrieval-augmented generation (RAG), but most contemporary RAG workflows focus heavily on text while existing methods for video-based summarization focus on low-level scene understanding rather than high-level event semantics. To close this gap, we introduce , a benchmark consisting of expert-written articles and densely annotated videos that provide evidence for articles' claims, facilitating the integration of video into RAG pipelines and enabling the creation of in-depth content that is grounded in multimodal sources. We further propose Collaborative Article Generation (CAG), a novel interactive method for article creation from multiple videos. CAG leverages an iterative interaction between an r1-style reasoning model and a VideoLLM to draw higher-level inferences about the target event than is possible with VideoLLMs alone, which fixate on low-level visual features. We benchmark state-of-the-art VideoLLMs and CAG in both oracle retrieval and RAG settings and find that CAG consistently outperforms alternative methods, while suggesting intriguing avenues for future work."
}