Text summarization aims at generating a brief and accurate summary from lengthy text without changing the original semantics of the text. A novel summarization method called Add Sememe-Pointer Model (ASPM) is proposed in this paper. The ASPM applies the pointer network in the Seq2Seq framework to solve the out-of-vocabulary problem. Considering the polysemous phenomenon in Chinese, the pointer network model does not fully understand the text semantics, leading to the poor performance of the model. Our method also uses the sememe knowledge bases to train the word vector representation of polysemous words, which can accurately capture the specific meaning of a word in the context, and we annotate some polysemous words in the LCSTS dataset so that the method can better understand the semantic information of the words in the dataset. The experimental results show that the ASPM can achieve higher ROUGE scores and make the Chinese summary more readable.