SumHiS: Extractive Summarization Exploiting Hidden Structure
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917691686649856 |
|---|---|
| author | Pavel, Tikhonov Ianina, Anastasiya Malykh, Valentin |
| author_facet | Pavel, Tikhonov Ianina, Anastasiya Malykh, Valentin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SumHiS: Extractive Summarization Exploiting Hidden Structure Pavel, Tikhonov Ianina, Anastasiya Malykh, Valentin Computation and Language 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. |
| title | SumHiS: Extractive Summarization Exploiting Hidden Structure |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.08215 |