Video Summarization

Video summarization studies how to condense footage into useful keyframes, highlights, or text while preserving important content and event order. Approaches range from frame-importance scoring and diverse keyframe selection to multimodal vision-language models that combine visual, audio, and transcript cues, often using hierarchical event representations or progressive filtering for long videos. Methods are tested on datasets such as SumMe, TVSum, and YouCook2, as well as newer long-video and multimodal benchmarks, with measures of summary quality, grounding, and downstream usefulness. A central challenge is that fluent summaries can still omit or misorder events, and human preferences vary, so evaluation must better assess factuality, temporal grounding, and task relevance.