Papers › A Neural Attention Model for Abstractive Sentence Summarization
A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush, Sumit Chopra, Jason Weston
Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.
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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 |
|---|---|---|---|---|---|---|---|
| Extractive Text Summarization | DUC 2004 Task 1 | Abs | ROUGE-1 | 26.55 | #1 of 1 | Archive leaderboard | report |
| Extractive Text Summarization | DUC 2004 Task 1 | Abs | ROUGE-2 | 7.06 | #1 of 1 | Archive leaderboard | report |
| Extractive Text Summarization | DUC 2004 Task 1 | Abs | ROUGE-L | 22.05 | #1 of 1 | Archive leaderboard | report |
| Text Summarization | DUC 2004 Task 1 | Abs+ | ROUGE-1 | 28.18 | #11 of 13 | Archive leaderboard | report |
| Text Summarization | DUC 2004 Task 1 | Abs+ | ROUGE-2 | 8.49 | #11 of 13 | Archive leaderboard | report |
| Text Summarization | DUC 2004 Task 1 | Abs+ | ROUGE-L | 23.81 | #11 of 13 | Archive leaderboard | report |
| Text Summarization | DUC 2004 Task 1 | ABS | ROUGE-L | 22.05 | #13 of 13 | Archive leaderboard | report |
| Text Summarization | GigaWord | Abs+ | ROUGE-1 | 31 | #39 of 41 | Archive leaderboard | report |
| Text Summarization | GigaWord | Abs | ROUGE-1 | 30.88 | #40 of 41 | 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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