mlsanityData to 7 Oct 2026

Get To The Point: Summarization with Pointer-Generator Networks

arXiv 1704.04368 ↗First posted 14 Apr 2017cs.CLcited by 1,281

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Cited by 58 in Efficient LLM inference and serving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.

Key facts. Cited by 1,281 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Apr 2017.

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Citing papers per year: 2019: 2; 2020: 3; 2021: 306; 2022: 266; 2023: 224; 2024: 212; 2025: 150; 2026: 118.

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  1. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  2. 2020Learning to summarize from human feedbackcited by 2,562
  3. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  5. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  6. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  7. 2023Benchmarking Large Language Models for News Summarizationcited by 444
  8. 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 374
  9. 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 363
  10. 2021XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languagescited by 346
  11. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  12. 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
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  1. 2023Trusting Your Evidence: Hallucinate Less with Context-aware Decodingcited by 319
  2. 2024Break the Sequential Dependency of LLM Inference Using Lookahead Decodingcited by 295
  3. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
  4. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  5. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 272
  6. 2022Towards a Unified Multi-Dimensional Evaluator for Text Generationcited by 264
  7. 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 255
  8. 2021Measuring Attribution in Natural Language Generation Modelscited by 253
  9. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  10. 2022A Contrastive Framework for Neural Text Generationcited by 224
  11. 2021Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Triggercited by 203
  12. 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 193
  13. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  14. 2021BookSum: A Collection of Datasets for Long-form Narrative Summarizationcited by 184
  15. 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 174
  16. 2023Conformal Language Modelingcited by 163
  17. 2025Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMscited by 162
  18. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  19. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
  20. 2024DistiLLM: Towards Streamlined Distillation for Large Language Modelscited by 145
  21. 2024Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Mapscited by 140
  22. 2023MAGE: Machine-generated Text Detection in the Wildcited by 130
  23. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 124
  24. 2024Keyformer: KV Cache Reduction through Key Tokens Selection for Efficient Generative Inferencecited by 124
  25. 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
  26. 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Tokencited by 123
  27. 2022Pretrained Language Models for Text Generation: A Surveycited by 121
  28. 2022Calibrating Sequence likelihood Improves Conditional Language Generationcited by 121
  29. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  30. 2023SparQ Attention: Bandwidth-Efficient LLM Inferencecited by 119
  31. 2021HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generationcited by 117
  32. 2022Training Language Models with Memory Augmentationcited by 112
  33. 2022Adam Can Converge Without Any Modification On Update Rulescited by 112
  34. 2024Concise Thoughts: Impact of Output Length on LLM Reasoning and Costcited by 108
  35. 2022Task-aware Retrieval with Instructionscited by 107
  36. 2022Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generationcited by 106
  37. 2021Scale Efficiently: Insights from Pre-training and Fine-tuning Transformerscited by 105
  38. 2021MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarizationcited by 104

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