This paper addresses the problem of supervised video summarization by
formulating it as a sequence-to-sequence learning problem, where the input is a
sequence of original video frames, the output is a keyshot sequence. Our key
idea is to learn a deep summarization network with attention mechanism to mimic
the way of selecting the keyshots of human. To this end, we propose a novel
video summarization framework named Attentive encoder-decoder networks for
Video Summarization (AVS), in which the encoder uses a Bidirectional Long
Short-Term Memory (BiLSTM) to encode the contextual information among the input
video frames. As for the decoder, two attention-based LSTM networks are
explored by using additive and multiplicative objective functions,
respectively. Extensive experiments are conducted on three video summarization
benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the
superiority of the proposed AVS-based approaches against the state-of-the-art
approaches,with remarkable improvements from 0.8% to 3% on two
datasets,respectively..

本研究提出了一种名为 AVS 的新颖的基于注意力机制的编码器 - 解码器网络框架，该网络框架使用双向 LSTM 和注意力机制对原始视频帧进行编码和关键帧序列进行解码，实现对有监督视频摘要的问题的解决。经测试发现，该方法在 SumMe 和 TVSum 数据集上的表现明显优于现有方法，在两个数据集上分别从 0.8％到 3％都有了显著的提高。