Sequential Copying Networks
Table 2: Full length Rouge F1 evaluation on English Gigaword test set of zhou-EtAl:2017:Long zhou-EtAl:2017:Long and our internal test set. Results with ‡ mark are taken from the corresponding papers. The ABS baseline fails to run with the latest stable Torch framework, so we omit its performance on our internal test set.
['[BOLD] Models', 'Test set in zhou-EtAl:2017:Long\xa0zhou-EtAl:2017:Long  [BOLD] RG-1', 'Test set in zhou-EtAl:2017:Long\xa0zhou-EtAl:2017:Long  [BOLD] RG-2', 'Test set in zhou-EtAl:2017:Long\xa0zhou-EtAl:2017:Long  [BOLD] RG-L', 'Our internal test set  [BOLD] RG-1', 'Our internal test set  [BOLD] RG-2', 'Our internal test set  [BOLD] RG-L']
EMNLP test set, \citeauthorzhou-EtAl:2017: Long \shortcitezhou-EtAl:2017: Long test set and our internal test set. As indicated in these tables, SeqCopyNet performs better than all the baseline models. As previously mentioned, the performance of copying methods performs comparably to the s2s+att baseline on test sets of \citeauthorrush-chopra-weston:2015: EMNLP \shortciterush-chopra-weston:2015: EMNLP and \citeauthorzhou-EtAl:2017:Long \shortcitezhou-EtAl:2017: Long, since the rare words and numbers have already been replaced in the references.