  Although the problem of automatic video summarization has recently received a
lot of attention, the problem of creating a video summary that also highlights
elements relevant to a search query has been less studied. We address this
problem by posing query-relevant summarization as a video frame subset
selection problem, which lets us optimise for summaries which are
simultaneously diverse, representative of the entire video, and relevant to a
text query. We quantify relevance by measuring the distance between frames and
queries in a common textual-visual semantic embedding space induced by a neural
network. In addition, we extend the model to capture query-independent
properties, such as frame quality. We compare our method against previous state
of the art on textual-visual embeddings for thumbnail selection and show that
our model outperforms them on relevance prediction. Furthermore, we introduce a
new dataset, annotated with diversity and query-specific relevance labels. On
this dataset, we train and test our complete model for video summarization and
show that it outperforms standard baselines such as Maximal Marginal Relevance.
