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, OpenWebText, and Wikipedia.
“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
AGGREFACT is a factual consistency evaluation benchmark for new summarization, targeting CNN(/DM) and XSum.
“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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QuestEval: Summarization Asks for Fact-based Evaluation
Scialom, T., Dray, P.-A., Lamprier, S., Piwowarski, B., Staiano, J., Wang, A., & Gallinari, P. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.529 · Cited by 175 · see citing works
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
Sharma, E., Li, C., & Wang, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1212 · Cited by 173 · see citing works
TLDR: Extreme Summarization of Scientific Documents
Cachola, I., Lo, K., Cohan, A., & Weld, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.428 · Cited by 169 · see citing works
“To our knowledge, the only other existing dataset is XSUM, introduced by (Narayan et al., 2018).”
BackgroundMethodInfluentialMetaICL: Learning to Learn In Context
Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169 · see citing works
Can AI-Generated Text be Reliably Detected?
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023) · arXiv · doi:10.48550/arxiv.2303.11156 · Cited by 161 · see citing works
“We use 100 passages from the Extreme Summarization (XSum) dataset [35] for our evaluations.”
MethodA Survey of Natural Language Generation
Dong, C., Li, Y., Gong, H., Chen, M., Li, J., Shen, Y., & Yang, M. (2022) · ACM Computing Surveys · doi:10.1145/3554727 · Cited by 169 · see citing works
tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection
Peinelt, N., Nguyen, D., & Liakata, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.630 · Cited by 155 · see citing works
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
Huang, L., Wu, L., & Wang, L. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.457 · Cited by 159 · see citing works
Bastings, J., & Filippova, K. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.14 · Cited by 168 · see citing works
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
Chen, Y., Liu, Y., Liang, C., & Zhang, Y. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.449 · Cited by 183 · see citing works
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) · doi:10.18653/v1/2021.gem-1.10 · Cited by 159 · see citing works
Self-citationWikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
Ladhak, F., Durmus, E., Cardie, C., & McKeown, K. R. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.360 · Cited by 157 · see citing works
Defending ChatGPT against jailbreak attack via self-reminders
Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., & Wu, F. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00765-8 · Cited by 158 · see citing works
Deep reinforcement and transfer learning for abstractive text summarization: A review
Alomari, A., Idris, N. B., Sabri, A. Q. M., & Alsmadi, I. M. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101276 · Cited by 141 · see citing works
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
Zhu, C., Xu, R., Zeng, M., & Huang, X. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.19 · Cited by 139 · see citing works
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Su, H., Shi, W., Kasai, J., Wang, Y., Hu, Y., Mari Ostendorf, E., Yih, W.-T., Smith, N. A., Zettlemoyer, L., & Yu, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.71 · Cited by 136 · see citing works
Text Summarization with Pretrained EncodersPreprint
Liu, Y., & Lapata, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.08345 · Cited by 132 · see citing works
Self-citationCLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
Cao, S., & Wang, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.532 · Cited by 135 · see citing works
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Li, W., Xiao, X., Liu, J., Wu, H. R., Wang, H., & Du, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.555 · Cited by 130 · see citing works
Enhancing Factual Consistency of Abstractive Summarization
Zhu, C., Hinthorn, W., Xu, R., Zeng, Q., Zeng, M., Huang, X., & Jiang, M. (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.58 · Cited by 127 · see citing works
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Zhou, C., Neubig, G., Gu, J., Diab, M., Guzmán, F., Zettlemoyer, L., & Ghazvininejad, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.120 · Cited by 122 · see citing works
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Fabbri, A., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.187 · Cited by 119 · see citing works
Datasets for large language models: a comprehensive survey
Liu, Y., Cao, J., Liu, C., Ding, K., & Jin, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11403-7 · Cited by 159 · see citing works
Entity-level Factual Consistency of Abstractive Text Summarization
Nan, F., Nallapati, R. M., Wang, Z., dos Santos, C. N., Zhu, H., Zhang, D., McKeown, K. R., & Xiang, B. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.235 · Cited by 127 · see citing works
Towards a Unified Multi-Dimensional Evaluator for Text Generation
Zhong, M., Liu, Y., Yin, D., Mao, Y., Jiao, Y., Liu, P., Zhu, C., Ji, H., & Han, J. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.131 · Cited by 117 · see citing works
An Empirical Survey on Long Document Summarization: Datasets, Models, and Metrics
Koh, H. Y., Ju, J., Liu, M., & Pan, S. (2022) · ACM Computing Surveys · doi:10.1145/3545176 · Cited by 124 · see citing works
A survey of multilingual large language models
Qin, L., Chen, Q., Zhou, Y., Zhi, C., Li, Y., Liao, L., Li, M., Che, W., & Yu, P. S. (2025) · Patterns · doi:10.1016/j.patter.2024.101118 · Cited by 197 · see citing works
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
Ye, Q., Lin, B. Y., & Ren, X. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.572 · Cited by 120 · see citing works
Few-Shot Text Generation with Natural Language Instructions
Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.32 · Cited by 107 · see citing works
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 101 · see citing works
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 112 · see citing works
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 105 · see citing works
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 102 · see citing works
Joshi, A., Fidalgo, E., Alegre, E., & Fernández-Robles, L. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118442 · Cited by 100 · see citing works
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 · see citing works
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 117 · see citing works
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 · see citing works
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 97 · see citing works
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 122 · see citing works
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 91 · see citing works
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 175 · see citing works
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 · see citing works
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 98 · see citing works
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 81 · see citing works
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 105 · see citing works
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 93 · see citing works
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 · see citing works
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 · 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. (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 · see citing works
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 · see citing works
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 · see citing works
Enhancing 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 78 · see citing works
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 79 · see citing works
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 · see citing works
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
Feng, X., Feng, X., Qin, L., Qin, B., & Liu, T. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.117 · Cited by 83 · see citing works
- 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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