Xgboost multiple class predictive performance beats one versus rest
Multiclass models in XGBoost consist of n_classes separate forests, one for each one-vs-rest binary problem.  At each iteration, an extra tree is added to each forest.  But it isn't actually a one-vs-rest approach (as I thought in the first version of this answer), because these trees are built to minimize a single loss function, the cross-entropy of the softmax probabilities.
https://discuss.xgboost.ai/t/multiclassification-training-process/29
https://github.com/dmlc/xgboost/issues/806
https://github.com/dmlc/xgboost/issues/3655  

In general, the one-vs-rest models are very good at identifying the single class, whereas the multiclass model has to balance performance on all of them.  More specifically, I think that the softmax may be responsible for the phenomenon you're displaying.  (I'm still thinking about it, but I thought I should post the above for now.)

Suppose one of your documents is reasonably likely to be in either of two topics: the probability scores given by the forests are 0.9, 0.85, then &lt;0.1 in all the rest.  In your topic-1 model, you make a fairly confident judgement that this document is of topic 1 (score of 0.9).  But in the multiclass ensemble, you see things as much more uncertain; maybe the model applies softmax, so that the probability of topic 1 is only ~0.5.

More extreme, suppose the individual topic model scores are all 0.9.  Now the multiclass ensemble applies softmax and produces equal 1/17 probabilities for each topic!

In the other direction, suppose one of your documents is judged unlikely to fit any of the topics: all the individual topic model probability scores are 0.01.  In the multiclass ensemble, that gets scaled up to 1/17 (OK, 17 topics makes this a harder sell).



Hrm, except how likely is it to get the 0.9 and 0.85, since a training sample in one of these two topics will be pushed toward 0 by the other model... ?  Especially when your scores are fairly high, so it's not like the models have huge blind spots.
(This part still causes a problem with the correct understanding of how XGBoost works; the log-loss of the softmax probabilities still penalizes being confident about belonging to two different classes...)