Automatic text summarization technology has a very broad application prospect in many fields, such as web search and browsing recommendation. The classic text summarization algorithm uses statistical methods to extract article keywords and topic sentences. It ignores semantic and grammatical information of the text to some extent. As distributed word vector embedding technology has been widely used in text summarization in recent years, we propose an automatic text summarization method based on word vector generation. This method mainly includes four modules: word vector generation, paragraph vector generation based on word vector, keyword extraction, and topic sentence extraction, through which an automatic text summarization of the document can finally be achieved. Experimental results show that the improved automatic text summarization method can extract topic sentences effectively.