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Overview
The Bart model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan
Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a
  left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme,
  where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It
