Abstract We address the problem of story-based temporal summarization of long 360◦ videos. We propose a novel memory network model named Past-Future Memory Network (PFMN), in which we fifirst compute the scores of 81 normal fifield of view (NFOV) region proposals cropped from the input 360◦ video, and then recover a latent, collective summary using the network with two external memories that store the embeddings of previously selected subshots and future candidate subshots. Our major contributions are twofold. First, our work is the fifirst to address story-based temporal summarization of 360◦ videos. Second, our model is the fifirst attempt to leverage memory networks for video summarization tasks. For evaluation, we perform three sets of experiments. First, we investigate the view selection capability of our model on the Pano2Vid dataset [42]. Second, we evaluate the temporal summarization with a newly collected 360◦ video dataset. Finally, we experiment our model’s performance in another domain, with image-based storytelling VIST dataset [22]. We verify that our model achieves state-of-the-art performance on all the tasks