  Recent research in AI is focusing towards generating narrative stories about
visual scenes. It has the potential to achieve more human-like understanding
than just basic description generation of images- in-sequence. In this work, we
propose a solution for generating stories for images-in-sequence that is based
on the Sequence to Sequence model. As a novelty, our encoder model is composed
of two separate encoders, one that models the behaviour of the image sequence
and other that models the sentence-story generated for the previous image in
the sequence of images. By using the image sequence encoder we capture the
temporal dependencies between the image sequence and the sentence-story and by
using the previous sentence-story encoder we achieve a better story flow. Our
solution generates long human-like stories that not only describe the visual
context of the image sequence but also contains narrative and evaluative
language. The obtained results were confirmed by manual human evaluation.
