  Traditional video summarization methods generate fixed video representations
regardless of user interest. Therefore such methods limit users' expectations
in content search and exploration scenarios. Multi-modal video summarization is
one of the methods utilized to address this problem. When multi-modal video
summarization is used to help video exploration, a text-based query is
considered as one of the main drivers of video summary generation, as it is
user-defined. Thus, encoding the text-based query and the video effectively are
both important for the task of multi-modal video summarization. In this work, a
new method is proposed that uses a specialized attention network and
contextualized word representations to tackle this task. The proposed model
consists of a contextualized video summary controller, multi-modal attention
mechanisms, an interactive attention network, and a video summary generator.
Based on the evaluation of the existing multi-modal video summarization
benchmark, experimental results show that the proposed model is effective with
the increase of +5.88% in accuracy and +4.06% increase of F1-score, compared
with the state-of-the-art method.
