Yes, the noising-denoising steps in this approach are summarizing-extending. The idea is that (unlike images) any piece of text lies somewhere on the length scale, like a fractal. You could take the whole text and treat it as a large scale object, or take shorter sentences and treat them on the short scale. What you are right in, is that you need a dataset of things being described twice. Like abstracts and papers, or messages and tldr's.
But for this approach adding "noise" would be much less straightforward, wouldn't it? You'd have to have some way to work out how to take "Berlin is the capital city of Germany with ..." down to an empty string in a way that marginally removes detail. For some passages this could be rather difficult to do. I feel like gathering training data would be a huge trial here.