Despite recent advances of AI, story understanding remains an open and under-investigated problem. We collect, preprocess, and publicly release a video-language story dataset, Synopses of Movie Narratives (SYMON), containing 5,193 video summaries of popular movies and TV series. SYMON captures naturalistic story-telling videos for human audience made by human creators. As a prototypical and naturalistic story dataset, SYMON features high coverage of multimodal story events, abundant mental-state descriptions, and large semantic gaps between the visual and the textual modalities. We establish benchmarks on video-text retrieval and zero-shot alignment on movie summary videos, which showcase the importance of in-domain data in story understanding. With SYMON, we hope to lay the groundwork for progress in multimodal story understanding.

翻译：尽管人工智能近期取得进展，故事理解仍然是一个开放且未充分研究的问题。我们收集、预处理并公开发布了一个视频语言故事数据集——电影叙事摘要（SYMON），包含5193个热门电影和电视剧的视频摘要。SYMON捕捉了人类创作者为人类观众制作的自然叙事视频。作为一个典型且自然的故事数据集，SYMON具有多模态故事事件的高覆盖率、丰富的心理状态描述以及视觉与文本模态之间较大的语义鸿沟。我们建立了视频文本检索和电影摘要视频的零样本对齐基准测试，这些测试展示了领域内数据在故事理解中的重要性。借助SYMON，我们希望为多模态故事理解的进展奠定基础。