mlsanityData to 7 Oct 2026

Bottom-Up Abstractive Summarization

arXiv 1808.10792 ↗First posted 31 Aug 2018cs.CL cs.AI cs.LGcited by 177

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Abstract (arXiv)

Neural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but which can be poor at content selection. This work proposes a simple technique for addressing this issue: use a data-efficient content selector to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences, making it easy to transfer a trained summarizer to a new domain.

Key facts. Cited by 177 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 75; 2022: 48; 2023: 27; 2024: 15; 2025: 9; 2026: 2.

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  1. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  2. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  3. 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 374
  4. 2021Efficient Attentions for Long Document Summarizationcited by 342
  5. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
  6. 2021DialogSum: A Real-Life Scenario Dialogue Summarization Datasetcited by 181
  7. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  8. 2022Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectorscited by 103
  9. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 101
  10. 2021SummScreen: A Dataset for Abstractive Screenplay Summarizationcited by 80
  11. 2023Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Methodcited by 76
  12. 2022An Empirical Survey on Long Document Summarization: Datasets, Models and Metricscited by 74
38 more
  1. 2021Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documentscited by 64
  2. 2021Can We Automate Scientific Reviewing?cited by 63
  3. 2021PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarizationcited by 62
  4. 2024Attribute First, then Generate: Locally-attributable Grounded Text Generationcited by 54
  5. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  6. 2021AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarizationcited by 51
  7. 2021D2S: Document-to-Slide Generation Via Query-Based Text Summarizationcited by 48
  8. 2022Conditional Generation with a Question-Answering Blueprintcited by 44
  9. 2022Prompted Opinion Summarization with GPT-3.5cited by 44
  10. 2022Long Document Summarization with Top-down and Bottom-up Inferencecited by 42
  11. 2021Automatic Text Evaluation through the Lens of Wasserstein Barycenterscited by 38
  12. 2022Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infillingcited by 38
  13. 2021Factual Consistency Evaluation for Text Summarization via Counterfactual Estimationcited by 37
  14. 2022Submodularity In Machine Learning and Artificial Intelligencecited by 37
  15. 2022ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generationcited by 37
  16. 2021Controllable Neural Dialogue Summarization with Personal Named Entity Planningcited by 36
  17. 2021What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarizationcited by 35
  18. 2021InfoLM: A New Metric to Evaluate Summarization & Data2Text Generationcited by 33
  19. 2021Bidimensional Leaderboards: Generate and Evaluate Language Hand in Handcited by 32
  20. 2022Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarizationcited by 32
  21. 2021Sequence Level Contrastive Learning for Text Summarizationcited by 31
  22. 2021Focus Attention: Promoting Faithfulness and Diversity in Summarizationcited by 30
  23. 2023MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarizationcited by 30
  24. 2021The Summary Loop: Learning to Write Abstractive Summaries Without Examplescited by 27
  25. 2021Controllable Abstractive Dialogue Summarization with Sketch Supervisioncited by 27
  26. 2022Towards Improving Faithfulness in Abstractive Summarizationcited by 27
  27. 2022ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generationcited by 26
  28. 2022Spurious Correlations in Reference-Free Evaluation of Text Generationcited by 26
  29. 2021RefSum: Refactoring Neural Summarizationcited by 25
  30. 2021Learning Opinion Summarizers by Selecting Informative Reviewscited by 25
  31. 2021Long Document Summarization in a Low Resource Setting using Pretrained Language Modelscited by 24
  32. 2021Long-Span Summarization via Local Attention and Content Selectioncited by 24
  33. 2021Topic-Aware Contrastive Learning for Abstractive Dialogue Summarizationcited by 23
  34. 2024Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Taskscited by 23
  35. 2021BASS: Boosting Abstractive Summarization with Unified Semantic Graphcited by 21
  36. 2021Enriching and Controlling Global Semantics for Text Summarizationcited by 20
  37. 2022Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarizationcited by 20
  38. 2021Topic-Guided Abstractive Multi-Document Summarizationcited by 19

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