This paper introduces a new, unsupervised method for automatic video
summarization using ideas from generative adversarial networks but eliminating
the discriminator, having a simple loss function, and separating training of
different parts of the model. An iterative training strategy is also applied by
alternately training the reconstructor and the frame selector for multiple
iterations. Furthermore, a trainable mask vector is added to the model in
summary generation during training and evaluation. The method also includes an
unsupervised model selection algorithm. Results from experiments on two public
datasets (SumMe and TVSum) and four datasets we created (Soccer, LoL, MLB, and
ShortMLB) demonstrate the effectiveness of each component on the model
performance, particularly the iterative training strategy. Evaluations and
comparisons with the state-of-the-art methods highlight the advantages of the
proposed method in performance, stability, and training efficiency.

该论文介绍了一种利用生成对抗网络的思想进行自动视频摘要的新的无监督方法，消除了鉴别器，使用简单的损失函数，并将模型的不同部分训练分离。采用迭代式训练策略，交替训练重构器和帧选择器多次迭代。此外，该方法在训练和评估期间添加了一个可训练的掩码向量到模型中进行摘要生成。在两个公共数据集 (SumMe 和 TVSum) 和四个我们创建的数据集 (Soccer、LoL、MLB 和 ShortMLB) 上的实验证明了每个组件在模型性能上的有效性，特别是迭代式训练策略。与最先进的方法进行的评估和比较突出了该方法在性能、稳定性和训练效率上的优势。