Papers › Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Jun Suzuki, Masaaki Nagata
This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Summarization | DUC 2004 Task 1 | EndDec+WFE | ROUGE-1 | 32.28 | #4 of 13 | Archive leaderboard | report |
| Text Summarization | DUC 2004 Task 1 | EndDec+WFE | ROUGE-2 | 10.54 | #4 of 13 | Archive leaderboard | report |
| Text Summarization | DUC 2004 Task 1 | EndDec+WFE | ROUGE-L | 27.8 | #4 of 13 | Archive leaderboard | report |
| Text Summarization | GigaWord | EndDec+WFE | ROUGE-1 | 36.30 | #32 of 41 | Archive leaderboard | report |
| Text Summarization | GigaWord | EndDec+WFE | ROUGE-2 | 17.31 | #32 of 41 | Archive leaderboard | report |
| Text Summarization | GigaWord | EndDec+WFE | ROUGE-L | 33.88 | #32 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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