Get To The Point: Summarization with Pointer-Generator Networks
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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.
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Relying on inference-time methods that are closer in behavior to context-based losses provides a reasonable solution to the trade-off between novelty and consistency when prioritizing control, as long as the language model's distribution is sufficiently confident in its decisions.
“this work found that relying on inference-time methods (Fan et al., 2018; Holtzman et al., 2019) that are closer in behavior to context-based losses (See et al., 2017; Welleck et al., 2019) provides a reasonable solution as long as the distribution of the language model is sufficiently confident in its decisions.”
From CTRL: A Conditional Transformer Language Model for Controllable Generation (2019) · cites this paper as See et al., 2017
The [91] model uses a multi-source encoder and an attention-and-copy decoder, and was tested on CoQA against baselines and ablations.
“A multi-source encoder along with a decoder based on attention and copy mechanism is employed for this task.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [93]
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Ladhak, F., Durmus, E., Suzgun, M., Zhang, T., Jurafsky, D., McKeown, K., & Hashimoto, T. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.234 · Cited by 30 · see citing works
Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization
Gunel, B., Zhu, C., Zeng, M., & Huang, X. (2020) · arXiv · doi:10.48550/arxiv.2006.15435 · Cited by 28 · see citing works
Predictive Comment Updating With Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach
Lin, B., Wang, S., Liu, Z., Xia, X., & Mao, X. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3185458 · Cited by 27 · see citing works
Feedback-based self-learning in large-scale conversational AI agents
Ponnusamy, P., Ghias, A., Yi, Y., Yao, B., Guo, C., & Sarikaya, R. (2022) · AI Magazine · doi:10.1609/aimag.v42i4.15102 · Cited by 19 · see citing works
Refocusing on Relevance: Personalization in NLG
Dudy, S., Bedrick, S., & Webber, B. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.421 · Cited by 17 · see citing works
Spurious Correlations in Reference-Free Evaluation of Text Generation
Durmus, E., Ladhak, F., & Hashimoto, T. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.102 · Cited by 16 · see citing works
Wait, But Why?: Assessing Behavior Explanation Strategies for Real-Time Strategy Games
Robertson, J., Kokkinakis, A. V., Hook, J., Kirman, B., Block, F., Ursu, M. F., Patra, S., Demediuk, S., Drachen, A., & Olarewaju, O. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450699 · Cited by 18 · see citing works
Pretrained Language Models for Text Generation: A Survey
Li, J., Tang, T., Zhao, W. X., & Wen, J.-R. (2021) · arXiv · doi:10.48550/arxiv.2105.10311 · Cited by 15 · see citing works
MAGE: Machine-generated Text Detection in the Wild
Li, Y., Li, Q., Cui, L., Bi, W., Wang, Z., Wang, L., Yang, L., Shi, S., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2305.13242 · Cited by 10 · see citing works
Wei, P., Yang, X., Liu, S., Wang, L., & Zheng, B. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track · doi:10.18653/v1/2022.naacl-industry.2 · Cited by 9 · see citing works
Fine-tuning BERT Models for Summarizing German Radiology Findings
Liang, S., Kades, K., Fink, M., Full, P., Weber, T., Kleesiek, J., Strube, M., & Maier-Hein, K. (2022) · Proceedings of the 4th Clinical Natural Language Processing Workshop · doi:10.18653/v1/2022.clinicalnlp-1.4 · Cited by 8 · see citing works
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., & Yih, S. W.-T. (2023) · arXiv · doi:10.48550/arxiv.2305.14739 · Cited by 6 · see citing works
“Summarization We conduct summarization experiments on two news datasets: CNN-DM (See et al., 2017) and XSUM (Narayan et al., 2018).”
MethodInfluentialExperience-guided multi-agent interpretable framework for radiology report summarization
Li, J., Zhou, T., Zhou, Z., Wei, X., Song, H., Wang, Z., Chen, Y., & Lv, H. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109078 · Cited by 4 · see citing works
Understanding Faithfulness and Reasoning of Large Language Models on Plain Biomedical Summaries
Fang, B., Dai, X., & Karimi, S. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.578 · Cited by 4 · see citing works
FormalAlign: Automated Alignment Evaluation for Autoformalization
Lu, J., Wan, Y., Huang, Y., Xiong, J., Liu, Z., & Guo, Z. (2024) · arXiv · doi:10.48550/arxiv.2410.10135 · Cited by 1 · see citing works
PAACE: A Plan-Aware Automated Agent Context Engineering Framework
Yuksel, K. A. (2025) · arXiv · doi:10.48550/arxiv.2512.16970
“Classical abstractive summarization models (Pointer-Generator (See et al., 2017), PEGASUS (Zhang et al., 2019), BART (Lewis et al., 2019), and T5 (Raffel et al., 2020)) enable high-level condensation of documents but lack task-awareness or plan-awareness.”
Chen, Y., Zhang, C., Luo, D., D'Haro, L. F., Tan, R. T., & Li, H. (2024) · arXiv · doi:10.48550/arxiv.2405.14646
Efficient Benchmarking (of Language Models)
Perlitz, Y., Bandel, E., Gera, A., Arviv, O., Ein-Dor, L., Shnarch, E., Slonim, N., Shmueli-Scheuer, M., & Choshen, L. (2023) · arXiv · doi:10.48550/arxiv.2308.11696
LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Li, S., Xu, C., Wang, J., Gong, X., Chen, C., Zhang, J., Wang, J., Lam, K.-Y., & Ji, S. (2025) · arXiv · doi:10.48550/arxiv.2506.09443
“We draw on English news articles from the CNN/DailyMail dataset [42].”
EditLens: Quantifying the Extent of AI Editing in Text
Thai, K., Emi, B., Masrour, E., & Iyyer, M. (2025) · arXiv · doi:10.48550/arxiv.2510.03154 · Cited by 3
“…from Amazon (Zhang et al., 2015) and Google (Li et al., 2022), creative writing samples from Reddit Writing Prompts (Fan et al., 2018), general educational web articles from FineWeb-EDU (Lozhkov et al., 2024), and news articles from XSum (Narayan et al., 2018) and CNN/DailyMail (See et al., 2017).”
- Authors
- See, A., Liu, P. J., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Google (United States) (US)
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1073-1083
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2606974598
- Cited by
- 3,775
- References
- 33
- Citations by year
- 2026: 126 · 2025: 230 · 2024: 292 · 2023: 490 · 2022: 516 · 2021: 758 · 2020: 603 · 2019: 520 · 2018: 220 · 2017: 20
- FWCI
- 170.02
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Defense Advanced Research Projects Agency
- Air Force Research Laboratory
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- abstractive summarization, pointer-generator network, coverage mechanism, sequence-to-sequence models, attention-based models, factual accuracy, repetition avoidance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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