Originality * This paper's main contribution of recall-precision balanced topic model is quite original, as no other topic model (AFAIK) tries to balance recall and precision, even though those are widely used and sensible metrics. However, I don't think the authors do enough; just saying that the sparse topic models are evaluated only from the perspective of maximizing recall does not automatically mean that they would do poorly on the precision dimension. I would have liked to see an empirical comparison with a sparse topic model, especially given that there are more advanced sparse models, such as Zhang, et al WWW2013. Quality * The experiments are done well, comparing the three models using a variety of metrics including recall/precision (KL based and conventional), topic coherence, adjusted rand index on classification, and topic entropy. Some of the non-conventional metrics are explained well.