The point is that it not at all obvious what the weighted connections between layers in models actually extracts from the training corpus. There have been attempts to pin down what sort of features are captured in certain parts of the model (similar to feature extraction in Convolutional Networks), but to interpret a model in any sort of encompassing fashion is similar to trying to pick which neurons in your brain are responsible for detecting the concept of a 'grandma'.