Twitter is a fast communication channel for gathering and spreading breaking news, and it generates a large volume of tweets for most events. Automatically creating a summary for an event is necessary and important. In this study, we explored two extractive approaches for summarizing events on Twitter. The first one exploits the semantic types of event related terms, and ranks the tweets based on the score computed from these semantic terms. The second one utilizes a graph convolutional network built from a tweet relation graph to generate tweet hidden features for tweet salience estimation. And the most salient tweets are selected as the summary of the event. Our experiments show that these two approaches outperform the compared methods.