Video summarization is a task which many researchers have tried to automate with deep learning methods. One of these methods is the SUM-GAN-AAE algorithm developed by Apostolidis et al. which is an unsupervised machine learning method evaluated in this study. The research aims at testing the algorithm's performance on the Breakfast dataset, which is an action localization dataset, and evaluate it with rank correlation coefficients. Parameter optimization was performed to tune the learning rate of the system according to the Breakfast dataset. Then, by using k-fold cross-validation, three metrics were used to evaluate the trained model - F-Score, Kendall's τ and Spearman's ρ. Analysis of the results indicates a high F-Score as reported by the SUM-GAN-AAE paper but low rank correlation coefficients. Moreover, plotting importance scores per frame demonstrates the algorithm's inability to select key frames. The findings suggest that F-Score is not a fitting metric to use in the context of video summarization and the SUM-GAN-AAE algorithm performs poorly not only on action localization datasets but also on video summarization ones such as SumMe.