  Pretrained language models based on Transformer architecture are the reason
for recent breakthroughs in many areas of NLP, including sentiment analysis,
question answering, named entity recognition. Headline generation is a special
kind of text summarization task. Models need to have strong natural language
understanding that goes beyond the meaning of individual words and sentences
and an ability to distinguish essential information to succeed in it. In this
paper, we fine-tune two pretrained Transformer-based models (mBART and
BertSumAbs) for that task and achieve new state-of-the-art results on the RIA
and Lenta datasets of Russian news. BertSumAbs increases ROUGE on average by
2.9 and 2.0 points respectively over previous best score achieved by
Phrase-Based Attentional Transformer and CopyNet.
