  Video data is explosively growing. As a result of the "big video data",
intelligent algorithms for automatic video summarization have re-emerged as a
pressing need. We develop a probabilistic model, Sequential and Hierarchical
Determinantal Point Process (SH-DPP), for query-focused extractive video
summarization. Given a user query and a long video sequence, our algorithm
returns a summary by selecting key shots from the video. The decision to
include a shot in the summary depends on the shot's relevance to the user query
and importance in the context of the video, jointly. We verify our approach on
two densely annotated video datasets. The query-focused video summarization is
particularly useful for search engines, e.g., to display snippets of videos.
