title: WikiVideo: Article Generation from Multiple Videos
summary: We present the challenging task of automatically creating a high-level
  Wikipedia-style article that aggregates information from multiple diverse
  videos about real-world events, such as natural disasters or political
  elections. Videos are intuitive sources for retrieval-augmented generation
  (RAG), but most contemporary RAG workflows focus heavily on text and existing
  methods for video-based summarization focus on low-level scene understanding
  rather than high-level event semantics. To close this gap, we introduce
  WikiVideo, 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.
paper_authors._id: 67efffab5e8b84102499ebfc
paper_authors.name: Alexander Martin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:53:55.294Z
paper_authors._id: 644ffb154f731658826945c8
paper_authors.avatarUrl: /avatars/46fa3a15c2b974692bee5b6ed6468be0.svg
paper_authors.fullname: Alex Martin
paper_authors.type: user
paper_authors.user: alexmartin1722
paper_authors._id: 67efffab5e8b84102499ebfd
paper_authors.name: Reno Kriz
paper_authors._id: 67efffab5e8b84102499ebfe
paper_authors.name: William Gantt Walden
paper_authors._id: 67efffab5e8b84102499ebff
paper_authors.name: Kate Sanders
paper_authors._id: 67efffab5e8b84102499ec00
paper_authors.name: Hannah Recknor
paper_authors._id: 67efffab5e8b84102499ec01
paper_authors.name: Eugene Yang
paper_authors._id: 67efffab5e8b84102499ec02
paper_authors.name: Francis Ferraro
paper_authors._id: 67efffab5e8b84102499ec03
paper_authors.name: Benjamin Van Durme
paper_title: WikiVideo: Article Generation from Multiple Videos
paper_summary: We present the challenging task of automatically creating a high-level
  Wikipedia-style article that aggregates information from multiple diverse
  videos about real-world events, such as natural disasters or political
  elections. Videos are intuitive sources for retrieval-augmented generation
  (RAG), but most contemporary RAG workflows focus heavily on text and existing
  methods for video-based summarization focus on low-level scene understanding
  rather than high-level event semantics. To close this gap, we introduce
  WikiVideo, 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.
paper_ai_summary: WikiVideo proposes Collaborative Article Generation (CAG) to enhance high-level event summarization from videos by integrating r1-style reasoning and VideoLLM for better inferences compared to state-of-the-art VideoLLMs.
paper_ai_keywords: retrieval-augmented generation RAG WikiVideo Collaborative Article Generation CAG r1-style reasoning model VideoLLM high-level event semantics video-based summarization multimodal sources low-level visual features oracle retrieval