A Study on the Use of Attention for Explaining Video Summarization

In this paper we present our study on the use of attention for explaining video summarization. We build on a recent work that formulates the task, called XAI-SUM, and we extend it by: a) taking into account two additional network architectures and b) introducing two novel explanation signals that relate to the entropy and diversity of attention weights. In total, we examine the effectiveness of seven types of explanation, using three state-of-the-art attention-based network architectures (CA-SUM, VASNet, SUM-GDA) and two datasets (SumMe, TVSum) for video summarization. The conducted evaluations show that the inherent attention weights are more suitable for explaining network architectures which integrate mechanisms for estimating attentive diversity (SUM-GDA) and uniqueness (CA-SUM). The explanation of simpler architectures (VASNet) can benefit from taking into account estimates about the strength of the input vectors, while another option is to consider the entropy of attention weights.