  Query-based video summarization is the task of creating a brief visual
trailer, which captures the parts of the video (or a collection of videos) that
are most relevant to the user-issued query. In this paper, we propose an
unsupervised label propagation approach for this task. Our approach effectively
captures the multimodal semantics of queries and videos using state-of-the-art
deep neural networks and creates a summary that is both semantically coherent
and visually attractive. We describe the theoretical framework of our
graph-based approach and empirically evaluate its effectiveness in creating
relevant and attractive trailers. Finally, we showcase example video trailers
generated by our system.
