  We explore the notion of uncertainty in the context of modern abstractive
summarization models, using the tools of Bayesian Deep Learning. Our approach
approximates Bayesian inference by first extending state-of-the-art
summarization models with Monte Carlo dropout and then using them to perform
multiple stochastic forward passes. Based on Bayesian inference we are able to
effectively quantify uncertainty at prediction time. Having a reliable
uncertainty measure, we can improve the experience of the end user by filtering
out generated summaries of high uncertainty. Furthermore, uncertainty
estimation could be used as a criterion for selecting samples for annotation,
and can be paired nicely with active learning and human-in-the-loop approaches.
Finally, Bayesian inference enables us to find a Bayesian summary which
performs better than a deterministic one and is more robust to uncertainty. In
practice, we show that our Variational Bayesian equivalents of BART and PEGASUS
can outperform their deterministic counterparts on multiple benchmark datasets.
