Update summarization can be a daunting task in automatic text summarization. It aims to distill evolved messages from a collection of new articles, under the assumption that the reader has already browsed the previous articles. In this paper, a number of state-of-the-art approaches were reviewed for extracting update summarization and then a method called DPC-TS was proposed. This approach is derived from the DPC-based multi-documents summarization method, which was developed in 2015. In our study, a model was established by using the topic signature algorithm, to evaluate the novelty of sentences in update documents set. In addition, the scheme of calculating similarities between sentences and sentence selection approach was improved to ensure there are diversity topics in the summary. DPC-TS update documents summarization method is a simple and effective approach, which can be reproduced and deployed in real environment.