Xiaodong Yan1,2, Yiqin Wang1,2, Wei Song1,2,*, Xiaobing Zhao1,2, A. Run3, Yang Yanxing4
CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1769-1781, 2022, DOI:10.32604/cmc.2022.027301
Abstract Text summarization creates subset that represents the most important or relevant information in the original content, which effectively reduce information redundancy. Recently neural network method has achieved good results in the task of text summarization both in Chinese and English, but the research of text summarization in low-resource languages is still in the exploratory stage, especially in Tibetan. What’s more, there is no large-scale annotated corpus for text summarization. The lack of dataset severely limits the development of low-resource text summarization. In this case, unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.… More >
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