@InProceedings{yu-bansal-berg:2017:EMNLP2017,
  author    = {Yu, Licheng  and  Bansal, Mohit  and  Berg, Tamara},
  title     = {Hierarchically-Attentive RNN for Album Summarization and Storytelling},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {966--971},
  abstract  = {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.},
  url       = {https://www.aclweb.org/anthology/D17-1101}
}