The DOI matches its Crossref record; 43 of the 46 references it cites were found in a registry or an index.
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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Flagged first, then by citations
3 of the 46 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
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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
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