Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking (2022.findings-naacl)
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| Challenge: | Abstractive summarization systems generate paraphrases, but they often contain information inconsistent with the source text. |
| Approach: | They propose to generate factually inconsistent summaries using source texts and reference summary with key information masked to train a factual consistency classifier. |
| Outcome: | The proposed method outperforms existing models and shows a competitive correlation with human judgments. |
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