We have a few huge corpus, in the order of tens or millions of documents. Training is costly. The question here is:
Do we really need to train with the whole corpus ? Are topics much better than if training with say a maximum of 2 M documents ??? This should be studied because if no improvement is gained when training with very large corpora we could sample the training set, and then carry out inference on the whole set when calculating the indicators.