Papers by Alexios Gidiotis
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue (2022.findings-acl)
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| Challenge: | Xu et al., 2019; Lewis e t al, 2019) show that Bayesian summarization methods can generate high quality summaries but suffer from a couple of issues when inputs lie far from the training data distribution. |
| Approach: | They propose to extend state-of-the-art summarization models with Monte Carlo dropout and perform multiple stochastic forward passes to approximate Bayesian inference. |
| Outcome: | The proposed method outperforms deterministic summarization models on multiple benchmark datasets. |