SumHiS: Extractive Summarization Exploiting Hidden Structure

• URL: http://arxiv.org/abs/2406.08215v1
• Date: Wed, 12 Jun 2024 13:44:58 GMT
• Title: SumHiS: Extractive Summarization Exploiting Hidden Structure
• Authors: Tikhonov Pavel, Anastasiya Ianina, Valentin Malykh,
• Abstract summary: 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.
• Score: 4.445432761373431
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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