Video hyperlinking offers a way to explore a video collection, making use of links that connect segments having related content. Hyperlinking systems thus seek to automatically create links by connecting given anchor segments to relevant targets within the collection. In this paper, we further investigate multimodal representations of video segments in a hyper-linking system based on bidirectional deep neural networks, which achieved state-of-the-art results in the TRECVid 2016 evaluation. A systematic study of different input representations is done with a focus on the aggregation of the representation of multiple keyframes. This includes, in particular, the use of memory vectors as a novel aggregation technique, which provides a significant improvement over other aggre-gation methods on the final hyperlinking task. Additionally, the use of metadata is investigated leading to increased performance and lower computational requirements for the system.