  We present a neural model for generating short stories from image sequences,
which extends the image description model by Vinyals et al. (Vinyals et al.,
2015). This extension relies on an encoder LSTM to compute a context vector of
each story from the image sequence. This context vector is used as the first
state of multiple independent decoder LSTMs, each of which generates the
portion of the story corresponding to each image in the sequence by taking the
image embedding as the first input. Our model showed competitive results with
the METEOR metric and human ratings in the internal track of the Visual
Storytelling Challenge 2018.
