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Controllable Abstractive Summarization

arXiv 1711.05217 ↗First posted 14 Nov 2017cs.CLcited by 110

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Abstract (arXiv)

Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, our system can produce high quality summaries that follow user preferences. Without user input, we set the control variables automatically. On the full text CNN-Dailymail dataset, we outperform state of the art abstractive systems (both in terms of F1-ROUGE1 40.38 vs. 39.53 and human evaluation).

Key facts. Cited by 110 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Nov 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 35; 2022: 28; 2023: 20; 2024: 17; 2025: 7; 2026: 3.

110 papers.

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  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  2. 2021Entity-level Factual Consistency of Abstractive Text Summarizationcited by 118
  3. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  4. 2021Planning with Learned Entity Prompts for Abstractive Summarizationcited by 75
  5. 2023ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfercited by 50
  6. 2021Aspect-Controllable Opinion Summarizationcited by 47
  7. 2024Following Length Constraints in Instructionscited by 46
  8. 2021Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformerscited by 43
  9. 2021MReD: A Meta-Review Dataset for Structure-Controllable Text Generationcited by 42
  10. 2022BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Banglacited by 42
  11. 2023Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systemscited by 37
  12. 2021Controllable Neural Dialogue Summarization with Personal Named Entity Planningcited by 36
38 more
  1. 2021Data-to-text Generation with Macro Planningcited by 34
  2. 2022Learning to Revise References for Faithful Summarizationcited by 28
  3. 2021Controllable Abstractive Dialogue Summarization with Sketch Supervisioncited by 27
  4. 2022NEWTS: A Corpus for News Topic-Focused Summarizationcited by 27
  5. 2021Learning Opinion Summarizers by Selecting Informative Reviewscited by 25
  6. 2024Privacy Preserving Prompt Engineering: A Surveycited by 25
  7. 2023Generating Summaries with Controllable Readability Levelscited by 24
  8. 2022EntSUM: A Data Set for Entity-Centric Summarizationcited by 22
  9. 2022Improving the Faithfulness of Abstractive Summarization via Entity Coverage Controlcited by 21
  10. 2022Why is constrained neural language generation particularly challenging?cited by 19
  11. 2022Readability Controllable Biomedical Document Summarizationcited by 19
  12. 2021Machine Translation Verbosity Control for Automatic Dubbingcited by 18
  13. 2021Comparative Opinion Summarization via Collaborative Decodingcited by 18
  14. 2021Isometric MT: Neural Machine Translation for Automatic Dubbingcited by 18
  15. 2023Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Surveycited by 18
  16. 2021Controllable Summarization with Constrained Markov Decision Processcited by 17
  17. 2022Data-to-text Generation with Variational Sequential Planningcited by 17
  18. 2022Socratic Pretraining: Question-Driven Pretraining for Controllable Summarizationcited by 17
  19. 2023From Chaos to Clarity: Claim Normalization to Empower Fact-Checkingcited by 17
  20. 2021LenAtten: An Effective Length Controlling Unit For Text Summarizationcited by 14
  21. 2022Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillationcited by 14
  22. 2023Prompt-Based Length Controlled Generation with Reinforcement Learningcited by 14
  23. 2021Text Compression-aided Transformer Encodingcited by 13
  24. 2021Inference Time Style Control for Summarizationcited by 13
  25. 2021A New Approach to Overgenerating and Scoring Abstractive Summariescited by 13
  26. 2022MACSum: Controllable Summarization with Mixed Attributescited by 13
  27. 2021HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Modelscited by 12
  28. 2025Behavioral Analysis of Information Salience in Large Language Modelscited by 12
  29. 2021Style Control for Schema-Guided Natural Language Generationcited by 10
  30. 2022SentBS: Sentence-level Beam Search for Controllable Summarizationcited by 10
  31. 2022Time-aware Prompting for Text Generationcited by 10
  32. 2024AmbigNLG: Addressing Task Ambiguity in Instruction for NLGcited by 10
  33. 2025ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructionscited by 10
  34. 2021Towards Generating Citation Sentences for Multiple References with Intent Controlcited by 9
  35. 2021Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrievalcited by 8
  36. 2022Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoningcited by 8
  37. 2023Enhancing Abstractiveness of Summarization Models through Calibrated Distillationcited by 8
  38. 2024Length Controlled Generation for Black-box LLMscited by 8

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