Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding
This provides a new dataset for researchers in multimodal AI to study story understanding, though it is incremental as it builds on existing video-language datasets.
The authors tackled the problem of story understanding in AI by creating and releasing the SyMoN dataset, which contains 5,193 video summaries of movies and TV series totaling 869 hours, and established benchmarks for video-text retrieval and zero-shot alignment.
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 with a total length of 869 hours. SyMoN captures naturalistic storytelling videos made by human creators and intended for a human audience. As a prototypical and naturalistic story dataset, SyMoN features high coverage of multimodal story events and abundant mental-state descriptions. Its use of storytelling techniques cause cross-domain semantic gaps that provide appropriate challenges to existing models. We establish benchmarks on video-text retrieval and zero-shot alignment on movie summary videos, which showcase the importance of in-domain data and long-term memory in story understanding. With SyMoN, we hope to lay the groundwork for progress in multimodal story understanding.