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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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Noobs at Semeval-2021 Task 4: Masked Language Modeling for abstract answer prediction
Shukla, S., Sarthak, S., & Arya, K. V. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.107
Abstractive Summarization Using Categorical Graph Network
Senthamizh Selvan., R. (2021) · Revista Gestão Inovação e Tecnologias · doi:10.47059/revistageintec.v11i4.2249
Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization
Zhang, X., & Liu, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84186-7_8
SummerTime: Text Summarization Toolkit for Non-expertsPreprint
Ni, A., Azerbayev, Z., Mutuma, M., Feng, T., Zhang, Y., Yu, T., Awadallah, A. H., & Radev, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12738
An Exploratory Study on Long Dialogue Summarization: What Works and What's NextPreprint
Zhang, Y., Ni, A., Yu, T., Zhang, R., Zhu, C., Deb, B., Çelikyılmaz, A., Awadallah, A. H., & Radev, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04609 · Cited by 10
A Scoring Model Assisted by Frequency for Multi-Document Summarization
Yu, Y., Wu, M., Su, W., & Cheung, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86383-8_25
Investigating Crowdsourcing Protocols for Evaluatingthe Factual Consistency of Summaries
Tang, X., Fabbri, A. R., Mao, Z., Adams, G., Wang, B., Li, H., Mehdad, Y., & Radev, D. (2021)
Concept-based Topic Attention for a Convolutional Sequence Document Summarization Model
Khanam, S. A., Liu, F., & Chen, Y. P. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533982
MReD: A Meta-Review Dataset for Controllable Text Generation.Preprint
Shen, C., Cheng, L., Zhou, R., Bing, L., You, Y., & Si, L. (2021) · arXiv (Cornell University)
Aspect-Oriented Summarization through Query-Focused Extraction.Preprint
Ahuja, O., Xu, J., Gupta, A., Horecka, K. M., & Durrett, G. (2021) · arXiv (Cornell University)
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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