  Pretraining techniques leveraging enormous datasets have driven recent
advances in text summarization. While folk explanations suggest that knowledge
transfer accounts for pretraining's benefits, little is known about why it
works or what makes a pretraining task or dataset suitable. In this paper, we
challenge the knowledge transfer story, showing that pretraining on documents
consisting of character n-grams selected at random, we can nearly match the
performance of models pretrained on real corpora. This work holds the promise
of eliminating upstream corpora, which may alleviate some concerns over
offensive language, bias, and copyright issues. To see whether the small
residual benefit of using real data could be accounted for by the structure of
the pretraining task, we design several tasks motivated by a qualitative study
of summarization corpora. However, these tasks confer no appreciable benefit,
leaving open the possibility of a small role for knowledge transfer.
