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.

Chinese features prominently in the Chinese communities located in the nations of Malay Archipelago. In these countries, Chinese has undergone the process of adjustment to the local languages and cultures, which leads to the occurrence of a Chinese variant in each country. In this paper, we conducted a quantitative analysis on Chinese news texts collected from five Malay Archipelago nations, namely Indonesia, Malaysia, Singapore, Philippines and Brunei, trying to figure out their differences with the texts written in modern standard Chinese from a lexical and syntactic perspective. The statistical results show that the Chinese variants used in these five nations are quite different, diverging from their modern Chinese mainland counterpart. Meanwhile, we managed to extract and classify several featured Chinese words used in each nation. All these discrepancies reflect how Chinese evolves overseas, and demonstrate the profound impact rom local societies and cultures on the development of Chinese.