基于Transformer的中文新闻摘要模型CNsum，提升生成质量。
CNsum:Automatic Summarization for Chinese News Text
- 采用Transformer结构专为中文新闻设计摘要模型
- 在THUCNews数据集上ROUGE得分优于基线模型
- 适合需要高质量中文摘要的NLP应用者
在大数据时代，高效获取有价值信息成为研究目标。文本摘要技术持续发展以满足这一需求。近期研究表明，基于Transformer的预训练语言模型在自然语言处理多项任务中取得显著成效。针对中文新闻文本摘要生成问题及Transformer在中文场景的应用，本文提出一种基于Transformer结构的中文新闻摘要模型CNsum，并在THUCNews等中文数据集上进行测试。实验结果表明，CNsum在ROUGE指标上优于基线模型，验证了该模型的有效性。
原文摘要 · Abstract (English)
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.
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