Papers › Deep Communicating Agents for Abstractive Summarization

Deep Communicating Agents for Abstractive Summarization

27 Mar 2018NAACL 2018 6arXiv:1803.10357archive 2025-07-28

Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi

We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.

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Tasks

Abstractive Text Summarization Decoder Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Abstractive Text Summarization	CNN / Daily Mail	DCA	ROUGE-1	41.69	#31 of 53	Archive leaderboard	report
Abstractive Text Summarization	CNN / Daily Mail	DCA	ROUGE-2	19.47	#31 of 53	Archive leaderboard	report
Abstractive Text Summarization	CNN / Daily Mail	DCA	ROUGE-L	37.92	#31 of 53	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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