Papers with abstractive summarization systems
FIZZ: Factual Inconsistency Detection by Zoom-in Summary and Zoom-out Document (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating factual consistency in abstractive summarization systems have significant limitations, especially on refinement and interpretability. |
| Approach: | They propose a method for detecting summary factual inconsistency based on fine-grained atomic facts decomposition and adaptive granularity expansion. |
| Outcome: | The proposed method outperforms existing systems on the AGGREFACT benchmark dataset and achieves state-of-the-art performance. |
Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization (2022.findings-emnlp)
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| Challenge: | Existing abstractive summarization systems are hampered by content hallucinations in which models generate text that is not directly inferable from the source alone. |
| Approach: | They propose to use external knowledge to latently connect entities and concepts to latences to lend provenance to many of these unfaithful yet factual entities. |
| Outcome: | The proposed model can be used to improve the factuality of summarizations without simply making them more extractive. |
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. |
Spurious Correlations in Reference-Free Evaluation of Text Generation (2022.acl-long)
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| Challenge: | Recent work suggests that reference-free evaluation metrics may rely on spurious correlations with human judgments. |
| Approach: | They propose to use model-based, reference-free evaluation metrics to evaluate natural language generation systems. |
| Outcome: | The proposed metrics achieve high correlations with human judgments, but they may not be robust enough to evaluate their efficacy and robustness. |
FREDSum: A Dialogue Summarization Corpus for French Political Debates (2023.findings-emnlp)
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| Challenge: | Recent advances in deep learning have improved the performance of abstractive summarization systems. |
| Approach: | They present a dataset of french political debates to enhance resources for multi-lingual dialogue summarization. |
| Outcome: | The proposed dataset will be made publicly available for use by the research community. |
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
| Challenge: | Recent advances in abstractive summarization systems produce factually inconsistent text . this is emphasized in tasks like summarizing, which often produce inconsistent text with no input article . |
| Approach: | They use reinforcement learning to optimize for factual consistency and explore trade-offs . they use textual-entailment rewards to optimize the accuracy of the generated summaries . |
| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency (2022.emnlp-main)
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| Challenge: | Abstractive summarization systems have a lack of a defined definition for the task . factual consistency is a key factor in summarizing, but there are still deficiencies . a new study shows that summarized summarisation models achieve improved performance . |
| Approach: | They propose a filtered summarization dataset with improved factual consistency to address this problem . they argue that the dataset should become a valid benchmark for developing and evaluating summarizing systems . |
| Outcome: | The proposed model improves on a popular summarization dataset with improved factual consistency. |
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. |
Learning with Rejection for Abstractive Text Summarization (2022.emnlp-main)
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| Challenge: | Existing abstractive summarization systems produce non-factual summaries due to noise in the training dataset. |
| Approach: | They propose a training objective for abstractive summarization based on rejection learning that learns whether or not to reject potentially noisy tokens. |
| Outcome: | The proposed method significantly improves the factuality of generated summaries in automatic and human evaluations when compared to baseline models. |