In this paper we present a video summarization method that extracts key-frames from industrial surveillance videos, thus dramatically reducing the number of frames without significant loss of semantic content. We propose to use the produced summaries as training set for neural network based Evaluative Rectification. Evaluative Rectification is a method that exploits an expert user's feedback regarding the correctness of an activity recognition framework on part of the data in order to enhance future classification results. The size of the training sample set usually depends on the topology of the network and on the complexity of the environment and activities observed. However, as is shown by the experiments conducted in a real-world industrial activity recognition dataset, using a much smaller but representative sample stemming from our summarization technique leads to significantly higher accuracy rates than those attained by a same size but randomly chosen set. To obtain comparable improvement in accuracy without the summarization technique, the experiments show that a far larger training sample set is needed, therefore requiring significantly increased human resources and computational cost.