SumHiS: Extractive Summarization Exploiting Hidden Structure
<|reference_start|>SumHiS: Extractive Summarization Exploiting Hidden Structure: 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.<|reference_end|>
@article{pavel2024sumhis:,
    title={SumHiS: Extractive Summarization Exploiting Hidden Structure},
    author={Tikhonov Pavel, Anastasiya Ianina, Valentin Malykh},
    journal={arXiv preprint arXiv:2406.08215},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08215},
    primaryClass={cs.CL}
}