CNsum：面向中文新闻文本的自动摘要
CNsum:Automatic Summarization for Chinese News Text
AI总结：
本文针对中文新闻摘要生成问题，提出基于Transformer结构的中文新闻文本摘要模型CNsum，并在THUCNews等数据集上验证其ROUGE分数优于基线模型。
摘要：
在大数据时代，从海量数据中高效获取有价值的信息已成为我们的研究目标。文本摘要技术为满足这一需求而不断发展。近期研究也表明，基于Transformer的预训练语言模型在自然语言处理（NLP）的各类任务中取得了巨大成功。针对中文新闻文本摘要生成问题以及Transformer结构在中文上的应用，本文提出了一种基于Transformer结构的中文新闻文本摘要模型（CNsum），并在THUCNews等中文数据集上进行了测试。实验结果表明，CNsum相比基线模型取得了更好的ROUGE分数，验证了该模型的优越性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
