Bertabs metrics lower than paper
I tested abstractive summarization pre-trained model using the source under transformers/examples/summarization/bertabs/...

My dataset are CNN & Daily mail, which are 30 thousands of docs.

However, the result of rouge score is as follows



****** ROUGE SCORES ******

** ROUGE 1
F1        >> 0.275
Precision >> 0.299
Recall    >> 0.260

** ROUGE 2
F1        >> 0.161
Precision >> 0.184
Recall    >> 0.149

** ROUGE L
F1        >> 0.305
Precision >> 0.326
Recall    >> 0.290


why is the result different from that of the article, Text Summarization with Pretrained Encoders ?

bump. i have the same question. paper says 5 BeamSize and better accuracy for bert-base. <|||||>same here<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
