Fine-grained Factual Consistency Assessment for Abstractive Summarization Models (2021.emnlp-main)
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| Challenge: | Recent studies have shown that around 30% of the summaries generated by abstractive summarization models contain factual errors. |
| Approach: | They propose a fine-grained two-stage Fact Consistency assessment framework for summarization models that uses fine-grain consistency reasoning to find subtle clues to identify whether a model-generated summary is consistent with the original document. |
| Outcome: | The proposed framework improves on the state-of-the-art models and distinguishes detailed differences better. |
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