A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
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Cited by 28 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.
Key facts. Cited by 477 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2018.
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Citing papers per year: 2021: 70; 2022: 83; 2023: 91; 2024: 79; 2025: 79; 2026: 75.
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