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Yang Liu
Abstract
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
Code Repositories
lingyu001/nlp_text_summarization_implementation
pytorch
Mentioned in GitHub
johnnyb1509/2020_VBDI_DL
tf
Mentioned in GitHub
nguyenphamvan/BertSum-master
pytorch
Mentioned in GitHub
vsubramaniam851/typ_embed
pytorch
Mentioned in GitHub
nakhunchumpolsathien/TR-TPBS
Mentioned in GitHub
aikawasho/BertSum
pytorch
Mentioned in GitHub
thangarani/bertsum
pytorch
Mentioned in GitHub
raqoon886/KoBertSum
pytorch
Mentioned in GitHub
raqoon886/KorBertSum
pytorch
Mentioned in GitHub
nlpyang/BertSum
Official
pytorch
Mentioned in GitHub
HHousen/TransformerSum
pytorch
Mentioned in GitHub
TidalPaladin/neural-summarizer
pytorch
Mentioned in GitHub
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| document-summarization-on-cnn-daily-mail | BERTSUM+Transformer | ROUGE-1: 43.25 ROUGE-2: 20.24 ROUGE-L: 39.63 |
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