Does Pretraining for Summarization Require Knowledge Transfer?
This work could eliminate the need for large upstream corpora in summarization, potentially addressing issues like bias and copyright, but it is incremental in questioning existing explanations.
The paper challenges the assumption that knowledge transfer is necessary for pretraining in text summarization, showing that models pretrained on random character n-grams nearly match the performance of those trained on real corpora, with only a small residual benefit from real data.
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.