  Recently, neural models have been proposed for headline generation by
learning to map documents to headlines with recurrent neural networks.
Nevertheless, as traditional neural network utilizes maximum likelihood
estimation for parameter optimization, it essentially constrains the expected
training objective within word level rather than sentence level. Moreover, the
performance of model prediction significantly relies on training data
distribution. To overcome these drawbacks, we employ minimum risk training
strategy in this paper, which directly optimizes model parameters in sentence
level with respect to evaluation metrics and leads to significant improvements
for headline generation. Experiment results show that our models outperforms
state-of-the-art systems on both English and Chinese headline generation tasks.
