Abstract
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 NLP. Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese texts, this paper proposes a Chinese news headline generation 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, BLEU and BERTScore scores than the baseline models, which verifies the outperformance of the model.
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Zhao, Y., Huang, S., Zhou, D., Ding, Z., Wang, F., Nian, A. (2022). CNsum: Automatic Summarization for Chinese News Text. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_45
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