title: WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
summary: Knowledge discovery and collection are intelligence-intensive tasks that
  traditionally require significant human effort to ensure high-quality outputs.
  Recent research has explored multi-agent frameworks for automating
  Wikipedia-style article generation by retrieving and synthesizing information
  from the internet. However, these methods primarily focus on text-only
  generation, overlooking the importance of multimodal content in enhancing
  informativeness and engagement. In this work, we introduce WikiAutoGen, a novel
  system for automated multimodal Wikipedia-style article generation. Unlike
  prior approaches, WikiAutoGen retrieves and integrates relevant images
  alongside text, enriching both the depth and visual appeal of generated
  content. To further improve factual accuracy and comprehensiveness, we propose
  a multi-perspective self-reflection mechanism, which critically assesses
  retrieved content from diverse viewpoints to enhance reliability, breadth, and
  coherence, etc. Additionally, we introduce WikiSeek, a benchmark comprising
  Wikipedia articles with topics paired with both textual and image-based
  representations, designed to evaluate multimodal knowledge generation on more
  challenging topics. Experimental results show that WikiAutoGen outperforms
  previous methods by 8%-29% on our WikiSeek benchmark, producing more accurate,
  coherent, and visually enriched Wikipedia-style articles. We show some of our
  generated examples in https://wikiautogen.github.io/ .
paper_authors._id: 67e3deabf2cb5de878fbf557
paper_authors.name: Zhongyu Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-26T20:44:00.382Z
paper_authors._id: 654f99f74c8874c64d4e5664
paper_authors.avatarUrl: /avatars/e9da0d688f91ae49db91d0ebebb3782a.svg
paper_authors.fullname: 杨忠裕
paper_authors.type: user
paper_authors.user: yzzyu
paper_authors._id: 67e3deabf2cb5de878fbf558
paper_authors.name: Jun Chen
paper_authors._id: 67e3deabf2cb5de878fbf559
paper_authors.name: Dannong Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T09:04:55.483Z
paper_authors._id: 66c0e1cdb83a7e94d5969f24
paper_authors.avatarUrl: /avatars/ee990b2e5fd4af08cd3691733e3a4e7c.svg
paper_authors.fullname: DanielX
paper_authors.type: user
paper_authors.user: DanielXu0208
paper_authors._id: 67e3deabf2cb5de878fbf55a
paper_authors.name: Junjie Fei
paper_authors._id: 67e3deabf2cb5de878fbf55b
paper_authors.name: Xiaoqian Shen
paper_authors._id: 67e3deabf2cb5de878fbf55c
paper_authors.name: Liangbing Zhao
paper_authors._id: 67e3deabf2cb5de878fbf55d
paper_authors.name: Chun-Mei Feng
paper_authors._id: 67e3deabf2cb5de878fbf55e
paper_authors.name: Mohamed Elhoseiny
paper_title: WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
paper_summary: Knowledge discovery and collection are intelligence-intensive tasks that
  traditionally require significant human effort to ensure high-quality outputs.
  Recent research has explored multi-agent frameworks for automating
  Wikipedia-style article generation by retrieving and synthesizing information
  from the internet. However, these methods primarily focus on text-only
  generation, overlooking the importance of multimodal content in enhancing
  informativeness and engagement. In this work, we introduce WikiAutoGen, a novel
  system for automated multimodal Wikipedia-style article generation. Unlike
  prior approaches, WikiAutoGen retrieves and integrates relevant images
  alongside text, enriching both the depth and visual appeal of generated
  content. To further improve factual accuracy and comprehensiveness, we propose
  a multi-perspective self-reflection mechanism, which critically assesses
  retrieved content from diverse viewpoints to enhance reliability, breadth, and
  coherence, etc. Additionally, we introduce WikiSeek, a benchmark comprising
  Wikipedia articles with topics paired with both textual and image-based
  representations, designed to evaluate multimodal knowledge generation on more
  challenging topics. Experimental results show that WikiAutoGen outperforms
  previous methods by 8%-29% on our WikiSeek benchmark, producing more accurate,
  coherent, and visually enriched Wikipedia-style articles. We show some of our
  generated examples in https://wikiautogen.github.io/ .
paper_ai_summary: WikiAutoGen automates the creation of multimodal Wikipedia-style articles by integrating text and images, using a multi-perspective self-reflection mechanism to improve accuracy and coherence.
paper_ai_keywords: WikiAutoGen multimodal content multi-perspective self-reflection WikiSeek factual accuracy comprehensiveness