  Text generation is an important Natural Language Processing task with various
applications. Although several metrics have already been introduced to evaluate
the text generation methods, each of them has its own shortcomings. The most
widely used metrics such as BLEU only consider the quality of generated
sentences and neglect their diversity. For example, repeatedly generation of
only one high quality sentence would result in a high BLEU score. On the other
hand, the more recent metric introduced to evaluate the diversity of generated
texts known as Self-BLEU ignores the quality of generated texts. In this paper,
we propose metrics to evaluate both the quality and diversity simultaneously by
approximating the distance of the learned generative model and the real data
distribution. For this purpose, we first introduce a metric that approximates
this distance using n-gram based measures. Then, a feature-based measure which
is based on a recent highly deep model trained on a large text corpus called
BERT is introduced. Finally, for oracle training mode in which the generator's
density can also be calculated, we propose to use the distance measures between
the corresponding explicit distributions. Eventually, the most popular and
recent text generation models are evaluated using both the existing and the
proposed metrics and the preferences of the proposed metrics are determined.
