Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports (2020.acl-main)
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| Challenge: | Existing abstractive summarization models do not guarantee factual correctness of summaries . |
| Approach: | They propose a framework where models evaluate factual correctness by fact-checking it against its reference using an information extraction module. |
| Outcome: | The proposed method significantly improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of human-authored ones. |
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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. |
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. |
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Annotating and Modeling Fine-grained Factuality in Summarization (2021.naacl-main)
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| Challenge: | Recent abstractive summarization systems produce factual errors that are not faithful to the input . current methods are lacking in identifying what errors are most important to target . |
| Approach: | They use synthetic and human-labeled data to identify factual errors in summarization and train models on the factuality detection task. |
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On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
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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. |
Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality (2023.acl-short)
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| Challenge: | Recent studies have shown that most abstractive summarization models are unfaithful and suffer from a wide range of hallucination. |
| Approach: | They propose a candidate summary generation and ranking technique to improve summary factuality without sacrificing quality. |
| Outcome: | The proposed method shows that the model trained using the proposed method improves on factuality and similarity-based metrics without conflicting with the model. |
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
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Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
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| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
Evaluating the Factual Consistency of Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | a weakly-supervised approach is needed to verify factual consistency . auxiliary span extraction tasks are useful for verifying factual consistent summaries . |
| Approach: | They propose a weakly-supervised approach for verifying factual consistency . they transfer the model to summaries generated by several neural models . |
| Outcome: | The proposed approach outperforms models trained with strong supervision on source documents and human evaluations. |
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (2021.naacl-main)
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| Challenge: | Modern summarization models generate fluent but often factually unreliable outputs. |
| Approach: | They propose to use human annotations to identify different categories of factual errors and benchmark factuality metrics to improve summarization evaluation. |
| Outcome: | The proposed method identifies the proportion of different categories of factual errors and benchmarks their human judgements as well as their specific strengths and weaknesses. |
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. |