Papers with correction models
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling (2022.emnlp-main)
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| Challenge: | Abstractive summarization models often generate inconsistent summaries containing factual errors or fabricated content. |
| Approach: | They propose to generate representative examples of non-factual summaries through infilling language models and train a robust fact-correction model to post-edit them to improve factual consistency. |
| Outcome: | The proposed model outperforms previous methods in correcting factual errors on two popular summarization datasets. |
Multi-Fact Correction in Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | Existing abstractive summarization systems generate incorrect facts with respect to the source text. |
| Approach: | They propose a suite of two factual correction models that leverages question-answering knowledge to make corrections in system-generated summaries via span selection. |
| Outcome: | The proposed model improves factuality of news summarization without sacrificing summary quality. |
Optimized Tokenization for Transcribed Error Correction (2023.emnlp-main)
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| Challenge: | transcribed-like data is often used to correct recurring errors, but training with synthetic data is difficult. |
| Approach: | They propose to use synthetic transcribed-like data to train error correction models . they show that synthetic data outperforms the common approach of random perturbations . |
| Outcome: | The proposed method outperforms the common method using random perturbations in transcribed data and language-specific adjustments to the vocabulary of a BPE tokenizer. |