mlsanityData to 7 Oct 2026

BRIO: Bringing Order to Abstractive Summarization

arXiv 2203.16804 ↗First posted 31 Mar 2022cs.CLcited by 161

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Abstract (arXiv)

Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary. This assumption may lead to performance degradation during inference, where the model needs to compare several system-generated (candidate) summaries that have deviated from the reference summary. To address this problem, we propose a novel training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability mass according to their quality. Our method achieves a new state-of-the-art result on the CNN/DailyMail (47.78 ROUGE-1) and XSum (49.07 ROUGE-1) datasets. Further analysis also shows that our model can estimate probabilities of candidate summaries that are more correlated with their level of quality.

Key facts. Cited by 161 papers in the map (data through 7 Oct 2026). Builds on 50 of its 74 references that are in the map. First posted on arXiv 31 Mar 2022.

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50 of 74 references.

References · 50

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38 more
  1. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
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  3. 2019When Does Label Smoothing Help?cited by 985
  4. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
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  6. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
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  8. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
  9. 2017A Deep Reinforced Model for Abstractive Summarizationcited by 364
  10. 2016Deep Reinforcement Learning for Dialogue Generationcited by 286
  11. 2020Extractive Summarization as Text Matchingcited by 189
  12. 2016An Actor-Critic Algorithm for Sequence Predictioncited by 158
  13. 2021SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarizationcited by 131
  14. 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
  15. 2016Sequence-to-Sequence Learning as Beam-Search Optimizationcited by 126
  16. 2019Bridging the Gap between Training and Inference for Neural Machine Translationcited by 123
  17. 2021CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarizationcited by 120
  18. 2015Minimum Risk Training for Neural Machine Translationcited by 114
  19. 2019On NMT Search Errors and Model Errors: Cat Got Your Tongue?cited by 114
  20. 2021Contrastive Learning for Many-to-many Multilingual Neural Machine Translationcited by 108
  21. 2020Few-Shot Text Generation with Pattern-Exploiting Trainingcited by 87
  22. 2019Beyond BLEU: Training Neural Machine Translation with Semantic Similaritycited by 84
  23. 2016Reward Augmented Maximum Likelihood for Neural Structured Predictioncited by 80
  24. 2019Calibration of Encoder Decoder Models for Neural Machine Translationcited by 67
  25. 2018Content Selection in Deep Learning Models of Summarizationcited by 64
  26. 2020Text Generation by Learning from Demonstrationscited by 64
  27. 2020On the Inference Calibration of Neural Machine Translationcited by 61
  28. 2020Contrastive Learning with Adversarial Perturbations for Conditional Text Generationcited by 56
  29. 2017Classical Structured Prediction Losses for Sequence to Sequence Learningcited by 55
  30. 2020Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentationcited by 53
  31. 2021ExplainaBoard: An Explainable Leaderboard for NLPcited by 50
  32. 2021Sequence Level Contrastive Learning for Text Summarizationcited by 31
  33. 2021RefSum: Refactoring Neural Summarizationcited by 25
  34. 2019Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarizationcited by 24
  35. 2020Simulated Multiple Reference Training Improves Low-Resource Machine Translationcited by 22
  36. 2019Contrastive Multi-document Question Generationcited by 15
  37. 2021Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarizationcited by 14
  38. 2015Multi-Document Summarization via Discriminative Summary Rerankingcited by 6

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Citing papers per year: 2022: 24; 2023: 77; 2024: 33; 2025: 20; 2026: 7.

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  1. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 454
  2. 2023Benchmarking Large Language Models for News Summarizationcited by 444
  3. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  4. 2023SLiC-HF: Sequence Likelihood Calibration with Human Feedbackcited by 395
  5. 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
  6. 2024MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documentscited by 251
  7. 2022Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluationcited by 141
  8. 2022Calibrating Sequence likelihood Improves Conditional Language Generationcited by 121
  9. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memorycited by 103
  10. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
  11. 2023A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4cited by 81
  12. 2023On Learning to Summarize with Large Language Models as Referencescited by 80
38 more
  1. 2023Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Methodcited by 76
  2. 2023Self-Evaluation Improves Selective Generation in Large Language Modelscited by 74
  3. 2022RankGen: Improving Text Generation with Large Ranking Modelscited by 72
  4. 2023Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedbackcited by 71
  5. 2023TIGERScore: Towards Building Explainable Metric for All Text Generation Taskscited by 69
  6. 2023Making Large Language Models Better Reasoners with Alignmentcited by 63
  7. 2023Extractive Summarization via ChatGPT for Faithful Summary Generationcited by 58
  8. 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
  9. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  10. 2023Detecting and Mitigating Hallucinations in Multilingual Summarisationcited by 48
  11. 2023Improving Large Language Model Fine-tuning for Solving Math Problemscited by 48
  12. 2023Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Modelscited by 46
  13. 2023Learning to Rank in Generative Retrievalcited by 46
  14. 2024LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversitycited by 42
  15. 2023Click: Controllable Text Generation with Sequence Likelihood Contrastive Learningcited by 41
  16. 2023On Context Utilization in Summarization with Large Language Modelscited by 30
  17. 2022Toward Unifying Text Segmentation and Long Document Summarizationcited by 28
  18. 2023Aligning Language Models with Human Preferences via a Bayesian Approachcited by 27
  19. 2023On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Studycited by 25
  20. 2022OASum: Large-Scale Open Domain Aspect-based Summarizationcited by 24
  21. 2023Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluationcited by 24
  22. 2024Understanding Reference Policies in Direct Preference Optimizationcited by 22
  23. 2023Domain-Agnostic Molecular Generation with Chemical Feedbackcited by 21
  24. 2022Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarizationcited by 20
  25. 2024ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publicationscited by 20
  26. 2022T5Score: Discriminative Fine-tuning of Generative Evaluation Metricscited by 18
  27. 2023AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrievalcited by 18
  28. 2022SMART: Sentences as Basic Units for Text Evaluationcited by 17
  29. 2024Contrastive Instruction Tuningcited by 17
  30. 2022Salience Allocation as Guidance for Abstractive Summarizationcited by 14
  31. 2024AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignmentcited by 14
  32. 2025Refine Knowledge of Large Language Models via Adaptive Contrastive Learningcited by 14
  33. 2023Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challengescited by 13
  34. 2022Analyzing and Evaluating Faithfulness in Dialogue Summarizationcited by 12
  35. 2023Semi-Offline Reinforcement Learning for Optimized Text Generationcited by 12
  36. 2025Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performancecited by 12
  37. 2023Should We Attend More or Less? Modulating Attention for Fairnesscited by 11
  38. 2024From Generalist to Specialist: A Survey of Large Language Models for Chemistrycited by 11

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