  One of the primary challenges of visual storytelling is developing techniques
that can maintain the context of the story over long event sequences to
generate human-like stories. In this paper, we propose a hierarchical deep
learning architecture based on encoder-decoder networks to address this
problem. To better help our network maintain this context while also generating
long and diverse sentences, we incorporate natural language image descriptions
along with the images themselves to generate each story sentence. We evaluate
our system on the Visual Storytelling (VIST) dataset and show that our method
outperforms state-of-the-art techniques on a suite of different automatic
evaluation metrics. The empirical results from this evaluation demonstrate the
necessities of different components of our proposed architecture and shows the
effectiveness of the architecture for visual storytelling.
