wehub-resource-syncPointer Generator Network for Text Summarization
This code is the Paddle v2.0 implementation of Get To The Point: Summarization with Pointer-Generator Networks. The code adapts and aligns with a previous Pytorch implementation.
To reach the state-of-the-art performance stated in the source paper, please use the default hyper-parameters listed in config.py.
Model performance (with pointer generation and coverage loss enabled)
After training for 100k iterations with batch_size=8, the Paddle implementation achieves a ROUGE-1-f1 of 0.3980 (0.3907 by a previous Pytorch implementation and 0.3953 by the source paper).
ROUGE-1:
rouge_1_f_score: 0.3980 with confidence interval (0.3959, 0.4002)
rouge_1_recall: 0.4639 with confidence interval (0.4613, 0.4667)
rouge_1_precision: 0.3707 with confidence interval (0.3683, 0.3732)
ROUGE-2:
rouge_2_f_score: 0.1726 with confidence interval (0.1704, 0.1749)
rouge_2_recall: 0.2008 with confidence interval (0.1984, 0.2034)
rouge_2_precision: 0.1615 with confidence interval (0.1593, 0.1638)
ROUGE-l:
rouge_l_f_score: 0.3617 with confidence interval (0.3597, 0.3640)
rouge_l_recall: 0.4214 with confidence interval (0.4188, 0.4242)
rouge_l_precision: 0.3371 with confidence interval (0.3348, 0.3396)