Papers › SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive...
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
Ramesh Nallapati, FeiFei Zhai, Bo-Wen Zhou
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.
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Code
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Code Syntology ran Syntology
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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 | CNN / Daily Mail (Anonymized) | SummaRuNNer | ROUGE-1 | 39.6 | #8 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | SummaRuNNer | ROUGE-2 | 16.2 | #8 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | SummaRuNNer | ROUGE-L | 35.3 | #8 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | Lead-3 baseline | ROUGE-1 | 39.2 | #9 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | Lead-3 baseline | ROUGE-2 | 15.7 | #9 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | Lead-3 baseline | ROUGE-L | 35.5 | #9 of 13 | 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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