Papers › Generative Adversarial Network for Abstractive Text Summarization

Generative Adversarial Network for Abstractive Text Summarization

26 Nov 2017arXiv:1711.09357archive 2025-07-28

Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, Hongyan Li

In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.

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Code

iwangjian/textsum-gan tf report

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Tasks

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

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Text Summarization	CNN / Daily Mail (Anonymized)	GAN	ROUGE-1	39.92	#5 of 13	Archive leaderboard	report
Text Summarization	CNN / Daily Mail (Anonymized)	GAN	ROUGE-2	17.65	#5 of 13	Archive leaderboard	report
Text Summarization	CNN / Daily Mail (Anonymized)	GAN	ROUGE-L	36.71	#5 of 13	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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