text: The article "Contextual Explanation Networks" introduces the class of models which learn the intermediate explanations in order to make final predictions. The contexts can be learned by, in principle, any model including neural networks, while the final predictions are supposed to be made by some simple models like linear ones. The probabilistic model allows for the simultaneous training of explanation and prediction parts as opposed to some recent post-hoc methods. The experimental part of the paper considers variety of experiments, including classification on MNIST, CIFAR-10, IMDB and also some experiments on survival analysis. I should note, that the quality of the algorithm is in general similar to other methods considered (as expected). However, while in some cases the CEN algorithm is slightly better, in other cases it appears to sufficiently loose, see for example left part of Figure 3(b) for MNIST data set. It would be interesting to know the explanation. Also, it would be interesting to have more examples of qualitative analysis to see, that the learned explanations are really useful. I am a bit worried, that while we have interpretability with respect to intermediate features, these features theirselves might be very hard to interpret. To sum up, I think that the general idea looks very natural and the results are quite supportive. However, I don't feel myself confident enough in this area of research to make strong conclusion on the quality of the paper.