Papers by Balaji Lakshminarayanan
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise (2023.findings-emnlp)
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| Challenge: | Abstractive summarization models are typically evaluated using test data that is identically distributed as training data. |
| Approach: | They propose a method to detect and remove input noise from documents to be summarized without extra training or auxiliary models. |
| Outcome: | The proposed method recovers a large fraction of the loss in performance, sometimes as large as 11 ROUGE-1 points, without extra training, auxiliary models, or prior knowledge of the type of noise. |