A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim +2 more
4/16/2018
cs.CL

Official code: armancohan/long-summarization (390 stars, Apache-2.0, last commit 2023-03-24).

Abstract

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.

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Apache-2.0

Resources for the NAACL 2018 paper "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents"

39058Shell, PythonApr 12, 20183 years agoApache-2.0

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Cite this paper

@article{cohan2018discourseaware,
  title  = {A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents},
  author = {Arman Cohan and Franck Dernoncourt and Doo Soon Kim and Trung Bui and Seokhwan Kim and Walter Chang and Nazli Goharian},
  year   = {2018},
  eprint = {1804.05685},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1804.05685}
}

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