  When video collections become huge, how to explore both within and across
videos efficiently is challenging. Video summarization is one of the ways to
tackle this issue. Traditional summarization approaches limit the effectiveness
of video exploration because they only generate one fixed video summary for a
given input video independent of the information need of the user. In this
work, we introduce a method which takes a text-based query as input and
generates a video summary corresponding to it. We do so by modeling video
summarization as a supervised learning problem and propose an end-to-end deep
learning based method for query-controllable video summarization to generate a
query-dependent video summary. Our proposed method consists of a video summary
controller, video summary generator, and video summary output module. To foster
the research of query-controllable video summarization and conduct our
experiments, we introduce a dataset that contains frame-based relevance score
labels. Based on our experimental result, it shows that the text-based query
helps control the video summary. It also shows the text-based query improves
our model performance. Our code and dataset:
https://github.com/Jhhuangkay/Query-controllable-Video-Summarization.
