Before revision:  This work considers distillation of BART and Pegasus , two state of the art  summarization models, on two datasets across a variety of student models. We produce high quality, fast checkpoints across different computational budgets, and learn some patterns about which distillation techniques perform well in which situations. PyTorch code to rerun our methods, and use the distilled BART and Pegasus checkpoints is available in Hugging Face transformers .
After revision:  This work considers distillation of BART and Pegasus , two state of the art summarization models, and find that SFT outperforms knowledge distillation and pseudo-labeling on the CNN/DailyMail dataset, but under-performs pseudo-labeling on the more abstractive XSUM dataset. PyTorch Code and checkpoints of different sizes are available through Hugging Face transformers .