  Document grounded generation is the task of using the information provided in
a document to improve text generation. This work focuses on two different
document grounded generation tasks: Wikipedia Update Generation task and
Dialogue response generation. Our work introduces two novel adaptations of
large scale pre-trained encoder-decoder models focusing on building context
driven representation of the document and enabling specific attention to the
information in the document. Additionally, we provide a stronger BART baseline
for these tasks. Our proposed techniques outperform existing methods on both
automated (at least 48% increase in BLEU-4 points) and human evaluation for
closeness to reference and relevance to the document. Furthermore, we perform
comprehensive manual inspection of the generated output and categorize errors
to provide insights into future directions in modeling these tasks.
