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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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- 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.
- Ramakanth Pasunuru and Mohit Bansal. 2018. Multireward reinforced summarization with saliency and entailment. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , New Orleans, USA.
- 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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To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text
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POS-Constrained Parallel Decoding for Non-autoregressive Generation
Yang, K., Lei, W., Liu, D., Qi, W., & Lv, J. (2021) · doi:10.18653/v1/2021.acl-long.467 · Cited by 12
Joint knowledge-powered topic level attention for a convolutional text summarization model
Khanam, S. A., Liu, F., & Chen, Y. P. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107273 · Cited by 10
Parallel dynamic topic modeling via evolving topic adjustment and term weighting scheme
Jiang, H., Lei, Z., Rao, Y., Xie, H., & Wang, F. L. (2021) · Information Sciences · doi:10.1016/j.ins.2021.11.060 · Cited by 9
Learning Disentangled Representation via Domain Adaptation for Dialogue Summarization
Li, J., Xia, Y., Cheng, X., Zhao, D., & Yan, R. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583389 · Cited by 10
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
Xiao, Y., Xu, R., Wu, L., Li, J., Qin, T., Liu, T., & Zhang, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26615 · Cited by 9
Generating EDU Extracts for Plan-Guided Summary Re-Ranking
Adams, G., Fabbri, A. R., Ladhak, F., Elhadad, N., & McKeown, K. R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.151 · Cited by 10
Improving abstractive summarization with energy-based re-ranking
Pernes, D., Mendes, A., & Martins, A. F. T. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.1 · Cited by 9
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
Wu, W., Li, W., Liu, J., Xiao, X., Cao, Z., Li, S., & Wu, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.267 · Cited by 9
Neural Knowledge Bank for Pretrained Transformers
Dai, D., Jiang, W., Dong, Q., Lyu, Y., & Sui, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44696-2_60 · Cited by 16
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization
Cao, S., & Wang, L. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.397 · Cited by 9
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
Xu, J., & Durrett, G. (2021) · doi:10.18653/v1/2021.acl-long.539 · Cited by 11
HG-News: News Headline Generation Based on a Generative Pre-Training Model
Li, P., Yu, J., Chen, J., & Guo, B. (2021) · IEEE Access · doi:10.1109/access.2021.3102741 · Cited by 10
Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative FlowPreprint
Gabriel, S., Bosselut, A., Holtzman, A., Lo, K., Çelikyılmaz, A., & Choi, Y. (2019) · arXiv (Cornell University) · Cited by 9
GLGE: A New General Language Generation Evaluation BenchmarkPreprint
Liu, D., Yan, Y., Gong, Y., Qi, W., Zhang, H., Jiao, J., Chen, W., Fu, J., Shou, L., Gong, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.11928 · Cited by 25
TSSuBERT: Tweet Stream Summarization Using BERTPreprint
Dusart, A., Pinel-Sauvagnat, K., & Hubert, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08770 · Cited by 12
Analyzing the Abstractiveness-Factuality Tradeoff With Nonlinear Abstractiveness Constraints.Preprint
Dreyer, M., Liu, M., Feng, N., Atluri, S., & Ravi, S. (2021) · arXiv (Cornell University) · Cited by 9
Varta: A Large-Scale Headline-Generation Dataset for Indic Languages
Aralikatte, R., Cheng, Z., Doddapaneni, S., & Cheung, J. C. K. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.215 · Cited by 9
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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