%0 Conference Proceedings %T Hierarchically-Attentive RNN for Album Summarization and Storytelling %A Yu, Licheng %A Bansal, Mohit %A Berg, Tamara %Y Palmer, Martha %Y Hwa, Rebecca %Y Riedel, Sebastian %S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing %D 2017 %8 September %I Association for Computational Linguistics %C Copenhagen, Denmark %F yu-etal-2017-hierarchically %X We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines. %R 10.18653/v1/D17-1101 %U https://aclanthology.org/D17-1101 %U https://doi.org/10.18653/v1/D17-1101 %P 966-971