SumHiS: Extractive Summarization Exploiting Hidden Sctructure

Abstract

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.

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