Papers › An Editorial Network for Enhanced Document Summarization
An Editorial Network for Enhanced Document Summarization
Edward Moroshko, Guy Feigenblat, Haggai Roitman, David Konopnicki
We suggest a new idea of Editorial Network - a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. Our network tries to imitate the decision process of a human editor during summarization. Within such a process, each extracted sentence may be either kept untouched, rephrased or completely rejected. We further suggest an effective way for training the "editor" based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baseline methods.
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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 | EditNet | ROUGE-1 | 41.42 | #34 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | EditNet | ROUGE-2 | 19.03 | #34 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | EditNet | ROUGE-L | 38.36 | #34 of 53 | 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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