mlsanityData to 7 Oct 2026

Counterfactual Data Augmentation improves Factuality of Abstractive Summarization

arXiv 2205.12416 ↗First posted 25 May 2022cs.CLcited by 7

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Abstract (arXiv)

Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences. In this paper, we present a counterfactual data augmentation approach where we augment data with perturbed summaries that increase the training data diversity. Specifically, we present three augmentation approaches based on replacing (i) entities from other and the same category and (ii) nouns with their corresponding WordNet hypernyms. We show that augmenting the training data with our approach improves the factual correctness of summaries without significantly affecting the ROUGE score. We show that in two commonly used summarization datasets (CNN/Dailymail and XSum), we improve the factual correctness by about 2.5 points on average

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 25 May 2022.

Builds on

18 of 27 references.

References · 18

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  4. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  5. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  6. 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
  7. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
  8. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
  9. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
  10. 2017Faithful to the Original: Fact Aware Neural Abstractive Summarizationcited by 141
  11. 2021Annotating and Modeling Fine-grained Factuality in Summarizationcited by 131
  12. 2021Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answeringcited by 131
6 more
  1. 2021CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarizationcited by 120
  2. 2021Entity-level Factual Consistency of Abstractive Text Summarizationcited by 118
  3. 2020Factual Error Correction for Abstractive Summarization Modelscited by 108
  4. 2019AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Datacited by 77
  5. 2020Multi-Fact Correction in Abstractive Text Summarizationcited by 66
  6. 2021Improving Factual Consistency of Abstractive Summarization via Question Answeringcited by 55

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 3.

7 papers.

  1. 2022Successive Prompting for Decomposing Complex Questionscited by 94
  2. 2022Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infillingcited by 38
  3. 2023Compositional Zero-Shot Domain Transfer with Text-to-Text Modelscited by 6
  4. 2022Realistic Data Augmentation Framework for Enhancing Tabular Reasoningcited by 4
  5. 2024De-amplifying Bias from Differential Privacy in Language Model Fine-tuningcited by 2
  6. 2024Text Grafting: Near-Distribution Weak Supervision for Minority Classes in Text Classificationcited by 2
  7. 2024Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Modelscited by 1