  Automatic summarization techniques aim to shorten and generalize information
given in the text while preserving its core message and the most relevant
ideas. This task can be approached and treated with a variety of methods,
however, not many attempts have been made to produce solutions specifically for
the Russian language despite existing localizations of the state-of-the-art
models. In this paper, we aim to showcase ruGPT3 ability to summarize texts,
fine-tuning it on the corpora of Russian news with their corresponding
human-generated summaries. Additionally, we employ hyperparameter tuning so
that the model's output becomes less random and more tied to the original text.
We evaluate the resulting texts with a set of metrics, showing that our
solution can surpass the state-of-the-art model's performance without
additional changes in architecture or loss function. Despite being able to
produce sensible summaries, our model still suffers from a number of flaws,
namely, it is prone to altering Named Entities present in the original text
(such as surnames, places, dates), deviating from facts stated in the given
document, and repeating the information in the summary.
