SumHiS: Extractive Summarization Exploiting Hidden Structure
This addresses the problem of generating accurate summaries for text processing applications, representing a strong specific gain.
The paper tackled extractive summarization by using hidden clustering structure in text, achieving state-of-the-art results on CNN/DailyMail with a 10% improvement in ROUGE-2 metric over previous approaches.
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.