  Extractive summarization is a task of highlighting the most important parts
of the text. We introduce a new approach to extractive summarization task using
hidden clustering structure of the text. Experimental results on CNN/DailyMail
demonstrate that our approach generates more accurate summaries than both
extractive and abstractive methods, achieving state-of-the-art results in terms
of ROUGE-2 metric exceeding the previous approaches by 10%. Additionally, we
show that hidden structure of the text could be interpreted as aspects.
