With the development of the Internet, how to quickly obtain core information from massive news and make browsing easy has become an urgent problem for information departments. The existing TextRank and its improved algorithm fail to consider text features comprehensively in extracting news summaries. In selecting summaries, they only focus on the redundancy and ignore the diversity and readability of the summaries. In order to solve the above problems, this study proposes a multi-feature automatic text summarization method, namely, MF-TextRank. A more comprehensive text feature information is summarized according to the structure, sentences, and words of news, which is used to improve the weight transfer matrix of the TextRank algorithm and make the sentence weight calculation more accurate. Furthermore, an MMR algorithm is used to update sentence weight, and the candidate summary set is obtained by beam search. According to the MMR score, the candidate summary set with the highest cohesion is selected as the final summary for output. The experimental results show that the MF-TextRank algorithm outperforms the existing improved TextRank algorithm in extracting summaries and effectively improves the accuracy in this regard.