We review three limitations of BLEU and ROUGE – the most popular metrics
 used to assess reference summaries against hypothesis summaries, come up with
 criteria for what a good metric should behave like and propose concrete ways to
 assess the performance of a metric in detail and show the potential of Transformers-based Language Models to assess reference summaries against hypothesis summaries.
New method for assessing the quaility of similarity evaluators and showing potential of Transformer-based language models in replacing BLEU and ROUGE.