mlsanityData to 7 Oct 2026

Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

arXiv 1808.08745 ↗First posted 27 Aug 2018cs.CLcited by 1,308

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Cited by 68 in Efficient LLM inference and serving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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 long-range 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.

Key facts. Cited by 1,308 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2018.

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Citing papers per year: 2020: 1; 2021: 145; 2022: 192; 2023: 249; 2024: 331; 2025: 243; 2026: 147.

1,308 papers.

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