Automated unsupervised video summarization by key-frame extraction consists in identifying representative video frames, best abridging a complete input sequence, and temporally ordering them to form a video summary, without relying on manually constructed ground-truth key-frame sets. State-of-the-art unsupervised deep neural approaches consider the desired summary to be a subset of the original sequence, composed of video frames that are sufficient to visually reconstruct the entire input. They typically employ a pre-trained CNN for extracting a vector representation per RGB video frame and a baseline LSTM adversarial learning framework for identifying key-frames. In this paper, to better guide the network towards properly selecting video frames that can faithfully reconstruct the original video, we augment the baseline framework with an additional LSTM autoencoder, which learns in parallel a fixed-length representation of the entire original input sequence. This is exploited during training, where a novel loss term inspired by dictionary learning is added to the network optimization objectives, further biasing key-frame selection towards video frames which are collectively able to recreate the original video. Empirical evaluation on two common public relevant datasets indicates highly favourable results.