Neural Headline Generation with Sentence-wise Optimization
Table 6: Comparison with baseline systems on Chinese test set. Note that the RNN-context has the same model architecture as ours. But they set a minimum length limit when decoding.
['System', 'Training', 'Model', 'LCSTS  [ITALIC] R1 [ITALIC] F1', 'LCSTS  [ITALIC] R2 [ITALIC] F1', 'LCSTS  [ITALIC] RLF1']
NHG with MRT improves the ROUGE scores up to over 3 points compared with baseline systems. This indicates that a character based model indeed performs good on Chinese summary task. Moreover, when evaluating with F1 scores, longer summaries would be penalized and get lower scores.