Papers › A Deep Reinforced Model for Abstractive Summarization
A Deep Reinforced Model for Abstractive Summarization
Romain Paulus, Caiming Xiong, Richard Socher
Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Summarization | CNN / Daily Mail | ML + RL (Paulus et al., 2017) | ROUGE-1 | 39.87 | #20 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | ML + RL (Paulus et al., 2017) | ROUGE-2 | 15.82 | #20 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | ML + RL (Paulus et al., 2017) | ROUGE-L | 36.90 | #20 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | ML + Intra-Attention (Paulus et al., 2017) | ROUGE-1 | 38.30 | #24 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | ML + Intra-Attention (Paulus et al., 2017) | ROUGE-2 | 14.81 | #24 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | ML + Intra-Attention (Paulus et al., 2017) | ROUGE-L | 35.49 | #24 of 26 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | ML+RL, with intra-attention | ROUGE-1 | 39.87 | #6 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | ML+RL, with intra-attention | ROUGE-2 | 15.82 | #6 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | ML+RL, with intra-attention | ROUGE-L | 36.90 | #6 of 13 | Archive leaderboard | report |
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