Papers with Neural abstractive summarization systems
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models often produce inconsistent statements or false facts. |
| Approach: | They propose an efficient weak-supervised adversarial data augmentation approach to generate factual consistency datasets by backpropagating gradients on token embeddings. |
| Outcome: | The proposed model can make interpretable factual errors tracing on public datasets and is cost-effective. |
Factual Error Correction for Abstractive Summarization Models (2020.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization are unable to ensure factual consistency of generated summaries. |
| Approach: | They propose a post-editing corrector module to identify and correct factual errors in generated summaries. |
| Outcome: | The proposed model outperforms existing models on CNN/DailyMail dataset on factual consistency evaluation. |