The network is able to overfit the dataset, given enough epochs. So even if it performs great it doesn't mean that it will behave this way with unknown data.
Assuming that the accuracy is the right metric for your problem, you have to take into account only the results on the test set. These show the performance of your model in unknown data.
This is the reason you haven't seen any studies focusing on the training set.
Check out this article for a more in depth explanation of overfitting
Editing just to add that, as far as I know most studies will report results for a test set that is created by splitting the original dataset. Using more datasets with slightly different features and testing there, could also indicate how well your model is able to generalize. That is because most datasets are actually not representative of the whole problem domain.
For example, if you train a hate speech model with twitter posts, which were collected using specific hashtags (keywords for events etc.), it might be useful to also test the same trained model in other twitter hate speech test sets that might've been collected in a different way (different events, different time period etc). In my experience, this is something that makes a stronger case for your model and also guides you to find the best combination of both model architecture and dataset to train it on, in order for the model to approximate better the whole domain of expertise.
