  While reinforcement learning can effectively improve language generation
models, it often suffers from generating incoherent and repetitive phrases
\cite{paulus2017deep}. In this paper, we propose a novel repetition normalized
adversarial reward to mitigate these problems. Our repetition penalized reward
can greatly reduce the repetition rate and adversarial training mitigates
generating incoherent phrases. Our model significantly outperforms the baseline
model on ROUGE-1\,(+3.24), ROUGE-L\,(+2.25), and a decreased repetition-rate
(-4.98\%).
