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Recent research in AI is focusing towards generating narrative stories aboutvisual scenes. It has the potential to achieve more human-like understandingthan just basic description generation of images- in-sequence. In this work, wepropose a solution for generating stories for images-in-sequence that is basedon the Sequence to Sequence model. As a novelty, our encoder model is composedof two separate encoders, one that models the behaviour of the image sequenceand other that models the sentence-story generated for the previous image inthe sequence of images. By using the image sequence encoder we capture thetemporal dependencies between the image sequence and the sentence-story and byusing the previous sentence-story encoder we achieve a better story flow. Oursolution generates long human-like stories that not only describe the visualcontext of the image sequence but also contains narrative and evaluativelanguage. The obtained results were confirmed by manual human evaluation.

Stories for Images-in-Sequence by using Visual and Narrative Components