Papers › Fine-tune BERT for Extractive Summarization
Fine-tune BERT for Extractive Summarization
Yang Liu
BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ROUGE-L. The codes to reproduce our results are available at https://github.com/nlpyang/BertSum
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Code
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Code Syntology ran Syntology
5 samples harvested; 2 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Summarization | CNN / Daily Mail | BERTSUM+Transformer | ROUGE-1 | 43.25 | #12 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | BERTSUM+Transformer | ROUGE-2 | 20.24 | #12 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | BERTSUM+Transformer | ROUGE-L | 39.63 | #12 of 26 | 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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