If we were to invent lm() now, how would we solve the problem of big P? I don't think we would use stars.
Assuming that this is a good idea in the first place, here's a simple solution, in the context of backward selection. One could sort the terms, from lowest p-value to highest p-value. If each variable is associated with more than one parameter (e.g. interactions), then it complicates things, however, the same principle applies. It would be possible to group terms, based on their significance level, however, this is unlikely to be popular. You could also use a head() and tail() approach, something I've been using a lot, in other contexts. However, I think a better solution is to automate the backward selection process, however, that requires decision rules, and we're back to the original problem.