I failed to replicate the results of the abs mission

[shellycsy]

Thank you for your sharing!
when i run the code
"python train.py -task abs -mode validate -test_all -bert_data_path /home/PreSumm/src/bert_data/ -ext_dropout 0.1 -model_path abs -lr 2e-3 -visible_gpus 1,2,3 -report_every 50 -save_checkpoint_steps 1000 -batch_size 500 -train_steps 50000 -accum_count 2 -log_file ../logs/ext_bert_cnndm -use_interval true -warmup_steps 10000 -max_pos 512" ,
and then i got the result:

1 ROUGE-1 Average_R: 0.33535 (95%-conf.int. 0.33294 - 0.33767)
1 ROUGE-1 Average_P: 0.43293 (95%-conf.int. 0.43023 - 0.43579)
1 ROUGE-1 Average_F: 0.36442 (95%-conf.int. 0.36217 - 0.36671)

1 ROUGE-2 Average_R: 0.14269 (95%-conf.int. 0.14078 - 0.14462)
1 ROUGE-2 Average_P: 0.18674 (95%-conf.int. 0.18429 - 0.18927)
1 ROUGE-2 Average_F: 0.15570 (95%-conf.int. 0.15358 - 0.15776)

1 ROUGE-L Average_R: 0.31230 (95%-conf.int. 0.30999 - 0.31456)
1 ROUGE-L Average_P: 0.40353 (95%-conf.int. 0.40070 - 0.40642)
1 ROUGE-L Average_F: 0.33950 (95%-conf.int. 0.33733 - 0.34176)

[2019-09-09 21:34:50,109 INFO] Rouges at step 46000

ROUGE-F(1/2/3/l): 36.44/15.57/33.95
ROUGE-R(1/2/3/l): 33.53/14.27/31.23

Why is this result so much lower than the one given in the paper ? But the results I got on the ext task were the same as in the paper.

[nlpyang]

please read readme carefully and use the command specifically for abstractive setting...

[shellycsy]

please read readme carefully and use the command specifically for abstractive setting...

I‘m sorry ,I expected the abs task to execute the same command as ext