Currently there are a lot of algorithms for video summarization; however most of them only represent visual information. In this paper, we propose two approaches for the construction of the summary using both video and text. One approach focuses on static summaries, where the summary is a set of selected keyframes and keywords, to be displayed in a fixed area. The second approach addresses dynamic summaries where video segments are selected based on both their visual and textual content to compose a new video sequence of predefined duration. Our approaches rely on an existing summarization algorithm, Video Maximal Marginal Relevance (Video-MMR), and its extension Text Video Maximal Marginal Relevance (TV-MMR) proposed by us. We describe the details of those approaches and present experimental results.