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        <title>Inverse Reinforcement Learning for Text Summarization</title>
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    <abstract>We introduce inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models, imitating human summarization behaviors. Our IRL model estimates the reward function using a suite of important sub-rewards for summarization and concurrently optimizes the policy network. Experimental results across datasets in different domains (CNN/DailyMail and WikiHow) and various model sizes (BART-base and BART-large) demonstrate the superiority of our proposed IRL model for summarization over MLE and RL baselines. The resulting summaries exhibit greater similarity to human-crafted gold references, outperforming MLE and RL baselines on metrics such as ROUGE, coverage, novelty, compression ratio, factuality, and human evaluations.</abstract>
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%0 Conference Proceedings
%T Inverse Reinforcement Learning for Text Summarization
%A Fu, Yu
%A Xiong, Deyi
%A Dong, Yue
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F fu-etal-2023-inverse
%X We introduce inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models, imitating human summarization behaviors. Our IRL model estimates the reward function using a suite of important sub-rewards for summarization and concurrently optimizes the policy network. Experimental results across datasets in different domains (CNN/DailyMail and WikiHow) and various model sizes (BART-base and BART-large) demonstrate the superiority of our proposed IRL model for summarization over MLE and RL baselines. The resulting summaries exhibit greater similarity to human-crafted gold references, outperforming MLE and RL baselines on metrics such as ROUGE, coverage, novelty, compression ratio, factuality, and human evaluations.
%R 10.18653/v1/2023.findings-emnlp.436
%U https://aclanthology.org/2023.findings-emnlp.436
%U https://doi.org/10.18653/v1/2023.findings-emnlp.436
%P 6559-6570

Markdown (Informal)

[Inverse Reinforcement Learning for Text Summarization](https://aclanthology.org/2023.findings-emnlp.436) (Fu et al., Findings 2023)

• Inverse Reinforcement Learning for Text Summarization (Fu et al., Findings 2023)

ACL

• Yu Fu, Deyi Xiong, and Yue Dong. 2023. Inverse Reinforcement Learning for Text Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6559–6570, Singapore. Association for Computational Linguistics.