Papers › Summary Level Training of Sentence Rewriting for Abstractive Summarization
Summary Level Training of Sentence Rewriting for Abstractive Summarization
Sanghwan Bae, Taeuk Kim, Jihoon Kim, Sang-goo Lee
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary. However, the existing models in this framework mostly rely on sentence-level rewards or suboptimal labels, causing a mismatch between a training objective and evaluation metric. In this paper, we present a novel training signal that directly maximizes summary-level ROUGE scores through reinforcement learning. In addition, we incorporate BERT into our model, making good use of its ability on natural language understanding. In extensive experiments, we show that a combination of our proposed model and training procedure obtains new state-of-the-art performance on both CNN/Daily Mail and New York Times datasets. We also demonstrate that it generalizes better on DUC-2002 test set.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Abstractive Text Summarization | CNN / Daily Mail | BERT-ext + abs + RL + rerank | ROUGE-1 | 41.90 | #28 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | BERT-ext + abs + RL + rerank | ROUGE-2 | 19.08 | #28 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | BERT-ext + abs + RL + rerank | ROUGE-L | 39.64 | #28 of 53 | Archive leaderboard | report |
| Extractive Text Summarization | CNN / Daily Mail | BERT-ext + RL | ROUGE-1 | 42.76 | #6 of 15 | Archive leaderboard | report |
| Extractive Text Summarization | CNN / Daily Mail | BERT-ext + RL | ROUGE-2 | 19.87 | #6 of 15 | Archive leaderboard | report |
| Extractive Text Summarization | CNN / Daily Mail | BERT-ext + RL | ROUGE-L | 39.11 | #6 of 15 | 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.
Methods
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