  Previous approaches for video summarization mainly concentrate on finding the
most diverse and representative visual contents as video summary without
considering the user's preference. This paper addresses the task of
query-focused video summarization, which takes user's query and a long video as
inputs and aims to generate a query-focused video summary. In this paper, we
consider the task as a problem of computing similarity between video shots and
query. To this end, we propose a method, named Convolutional Hierarchical
Attention Network (CHAN), which consists of two parts: feature encoding network
and query-relevance computing module. In the encoding network, we employ a
convolutional network with local self-attention mechanism and query-aware
global attention mechanism to learns visual information of each shot. The
encoded features will be sent to query-relevance computing module to generate
queryfocused video summary. Extensive experiments on the benchmark dataset
demonstrate the competitive performance and show the effectiveness of our
approach.
