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Although the problem of automatic video summarization has recently received alot of attention, the problem of creating a video summary that also highlightselements relevant to a search query has been less studied. We address thisproblem by posing query-relevant summarization as a video frame subsetselection problem, which lets us optimise for summaries which aresimultaneously diverse, representative of the entire video, and relevant to atext query. We quantify relevance by measuring the distance between frames andqueries in a common textual-visual semantic embedding space induced by a neuralnetwork. In addition, we extend the model to capture query-independentproperties, such as frame quality. We compare our method against previous stateof the art on textual-visual embeddings for thumbnail selection and show thatour model outperforms them on relevance prediction. Furthermore, we introduce anew dataset, annotated with diversity and query-specific relevance labels. Onthis dataset, we train and test our complete model for video summarization andshow that it outperforms standard baselines such as Maximal Marginal Relevance.

Query-adaptive Video Summarization via Quality-aware Relevance Estimation