CNsum:Automatic Summarization for Chinese News Text
This addresses the problem of efficient information extraction from Chinese news texts for NLP applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackles Chinese news text summarization by proposing CNsum, a Transformer-based model, and tests it on datasets like THUCNews, achieving better ROUGE scores than baseline models.
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.