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Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Open accessAbstract
We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach.The idea is to create a short, one-sentence news summary answering the question "What is the article about?".We collect a real-world, large scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC).We propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks.We demonstrate experimentally that this architecture captures longrange dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. 1
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“These benchmarks often originate from human-written datasets such as XSum [Narayan et al., 2018], OpenWebText [Gokaslan and Cohen, 2019], and Wikipedia.”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Narayan et al., 2018
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“AGGREFACT (Tang et al., 2023a) is a factual consistency evaluation benchmark for new summarization, targeting CNN(/DM) (Nallapati et al., 2016) and XSum (Narayan et al., 2018).”
From MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (2024) · cites this paper as Narayan et al., 2018
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Research works printed but not linked: 3
- Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018b. Ranking sentences for extractive summarization with reinforcement learning. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , New Orleans, USA.
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- Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointergenerator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics , pages 1073-1083, Vancouver, Canada.
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Scoring Sentence Singletons and Pairs for Abstractive Summarization
Lebanoff, L., Song, K., Dernoncourt, F., Kim, D. S., Kim, S., Chang, W. H., & Liu, F. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1209 · Cited by 104
Planning with Learned Entity Prompts for Abstractive Summarization
Narayan, S., Zhao, Y., Maynez, J., Simões, G., Nikolaev, V., & McDonald, R. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00438 · Cited by 124
Self-citationMulti-Fact Correction in Abstractive Text Summarization
Dong, Y., Wang, S., Gan, Z., Cheng, Y., Cheung, J. C. K., & Liu, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.749 · Cited by 115
Sequence Level Contrastive Learning for Text Summarization
Xu, S., Zhang, X., Wu, Y., & Wei, F. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21409 · Cited by 105
Joshi, A., Fidalgo, E., Alegre, E., & Fernández-Robles, L. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118442 · Cited by 103
Reducing Quantity Hallucinations in Abstractive Summarization
Zhao, Z., Cohen, S. B., & Webber, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.203 · Cited by 99
Self-citationA Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
Laskar, M. T. R., Bari, M. S., Rahman, M., Bhuiyan, M. A. H., Joty, S., & Huang, J. X. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.29 · Cited by 160
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
Kim, B., Kim, H., & Kim, G. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1260 · Cited by 96
Lu, Y., Dong, Y., & Charlin, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.648 · Cited by 113
BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization
Kryściński, W., Rajani, N. F., Agarwal, D., Xiong, C., & Radev, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.488 · Cited by 183
Su, D., Xu, Y., Yu, T., Siddique, F. B., Barezi, E. J., & Fung, P. (2020) · Workshop on NLP for COVID-19 (NLP-COVID19) · doi:10.18653/v1/2020.nlpcovid19-2.14 · Cited by 108
Benchmarking and Defending against Indirect Prompt Injection Attacks on Large Language Models
Yi, J., Xie, Y., Zhu, B., Kıcıman, E., Sun, G., Xie, X., & Wu, F. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709179 · Cited by 397
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
Chen, S., Zhang, F., Sone, K., & Roth, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.475 · Cited by 83
IndicBART: A Pre-trained Model for Indic Natural Language Generation
Dabre, R., Shrotriya, H., Kunchukuttan, A., Puduppully, R., Khapra, M. M., & Kumar, P. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.145 · Cited by 123
Keywords-Guided Abstractive Sentence Summarization
Li, H., Zhu, J., Zhang, J., Zong, C., & He, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6333 · Cited by 84
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text
Su, J., Zhuo, T. Y., Wang, D., & Nakov, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.827 · Cited by 192
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Yang, C., Zhu, Y. T., Wang, L., Wang, Y., Chen, Q., Gao, C., Yan, B., & Chen, Y. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3699518 · Cited by 128
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data
Filippova, K. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.76 · Cited by 79
Sanders, A. C., White, R., Severson, L. S., Ma, R., McQueen, R., Paulo, H. C. A., Zhang, Y., Erickson, J. S., & Bennett, K. P. (2020) · medRxiv · doi:10.1101/2020.08.28.20183863 · Cited by 79
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. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.3 · Cited by 79
Can We Automate Scientific Reviewing?
Yuan, W., Liu, P., & Neubig, G. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12862 · Cited by 96
Improving Factual Consistency of Abstractive Summarization via Question Answering
Nan, F., Nogueira dos Santos, C., Zhu, H., Ng, P., McKeown, K., Nallapati, R., Zhang, D., Wang, Z., Arnold, A. O., & Xiang, B. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.536 · Cited by 78
“We evaluate both Q UALS and QAGS on the same set of summaries generated by the MLE baseline model on the test set of documents in XSUM and CNNDM, respectively.”
MethodInfluentialEnhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks
Cui, P., Hu, L., & Liu, Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.468 · Cited by 79
Do Transformer Modifications Transfer Across Implementations and Applications?
Narang, S., Chung, H. W., Tay, Y., Fedus, L., Févry, T., Matena, M. S., Malkan, K., Fiedel, N., Shazeer, N., Lan, Z., et al. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.465 · Cited by 88
Paragraph-level Simplification of Medical Texts
Devaraj, A., Marshall, I., Wallace, B., & Li, J. J. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.395 · Cited by 79
Metadata
- Authors
- Narayan, S., Cohen, S. B., & Lapata, M.
- Affiliations
- University of Edinburgh (GB)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 1797-1807
- OpenAlex
- W2888482885
- Cited by
- 1,207
- References
- 46
- Citations by year
- 2026: 80 · 2025: 151 · 2024: 132 · 2023: 267 · 2022: 199 · 2021: 238 · 2020: 102 · 2019: 37 · 2018: 1
- FWCI
- 38.58
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- European Commission (HORIZON2020, 688139)
- H2020 European Research Council (681760)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- single-document summarization, abstractive summarization, convolutional neural networks, long-range dependencies, summarization datasets
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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