Abstract: With more and more text data generated by the Internet, the problem of text information overload is becoming more and more serious. It is very necessary to reduce the dimension of various texts, and text summarization is one of the important means, and it is also one of the hot and difficult points in the field of artificial intelligence research. Text is designed to transform a text or a collection of texts into a short summary containing key information. In recent years, language model preprocessing has improved the technical level of many natural language processing tasks, including emotion analysis, question and answer, natural language reasoning, named entity recognition, text similarity and text summarization. In this paper, the classic methods of text summarization in the past and the methods of text summarization based on pre-training in recent years are combed, and the data sets and evaluation methods of text summarization are sorted out. Finally, the challenges and development trends of text summarization are summarized.

Key words: datasets, text abstract, pre-training model