Fine-tune BERT for Extractive Summarization
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
PDF AbstractCode
CNN/Daily Mail
New York Times Annotated Corpus
Results from the Paper
Methods
Absolute Position Encodings •
Adam •
Attention Dropout •
BERT •
BPE •
Dense Connections •
Dropout •
GELU •
Label Smoothing •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
ReLU •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer •
Weight Decay •
WordPiece
