GRET: Global Representation Enhanced Transformer
Table 2: The comparison of our GRET , Transformer baseline and related work on the LCSTS text summarization task (* indicates the results came from their paper).
['Model', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
Our approach achieves considerable improvements in ROUGE-1/2/L (+1.63/+1.70/+1.49) and outperforms other work with same settings. The improvement on text summarization is even more than machine translation. Compared with machine translation, text summarization focuses more on extracting suitable information from the input sentence, which is an advantage of the GRET model.