Obtaining valuable information from massive data efficiently has become our research goal in the era of Big Data. Text summarization technology has been continuously developed to meet this demand. Recent work has also shown that transformer-based pre-trained language models have achieved great success on various tasks in Natural Language Processing (NLP). Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese, this paper proposes a Chinese news text summarization model (CNsum) based on Transformer structure, and tests it on Chinese datasets such as THUCNews. The results of the conducted experiments show that CNsum achieves better ROUGE score than the baseline models, which verifies the outperformance of the model.
@article{arxiv.2502.19723,
title = {CNsum:Automatic Summarization for Chinese News Text},
author = {Yu Zhao and Songping Huang and Dongsheng Zhou and Zhaoyun Ding and Fei Wang and Aixin Nian},
journal= {arXiv preprint arXiv:2502.19723},
year = {2025}
}
Comments
This withdrawal is due to the lack of authorization from all co-authors for the publication of this version