Uncertainty-Aware Abstractive Summarization
- URL: http://arxiv.org/abs/2105.10155v1
- Date: Fri, 21 May 2021 06:36:40 GMT
- Title: Uncertainty-Aware Abstractive Summarization
- Authors: Alexios Gidiotis and Grigorios Tsoumakas
- Abstract summary: We propose a novel approach to summarization based on Bayesian deep learning.
We show that our variational equivalents of BART and PEG can outperform their deterministic counterparts on multiple benchmark datasets.
Having a reliable uncertainty measure, we can improve the experience of the end user by filtering generated summaries of high uncertainty.
- Score: 3.1423034006764965
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a novel approach to summarization based on Bayesian deep learning.
We approximate Bayesian summary generation by first extending state-of-the-art
summarization models with Monte Carlo dropout and then using them to perform
multiple stochastic forward passes. This method allows us to improve
summarization performance by simply using the median of multiple stochastic
summaries. We show that our variational equivalents of BART and PEGASUS can
outperform their deterministic counterparts on multiple benchmark datasets. In
addition, we rely on Bayesian inference to measure the uncertainty of the model
when generating summaries. Having a reliable uncertainty measure, we can
improve the experience of the end user by filtering out generated summaries of
high uncertainty. Furthermore, our proposed metric 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.
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