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Previous work on visual storytelling mainly focused on exploring imagesequence as evidence for storytelling and neglected textual evidence forguiding story generation. Motivated by human storytelling process which recallsstories for familiar images, we exploit textual evidence from similar images tohelp generate coherent and meaningful stories. To pick the images which mayprovide textual experience, we propose a two-step ranking method based on imageobject recognition techniques. To utilize textual information, we design anextended Seq2Seq model with two-channel encoder and attention. Experiments onthe VIST dataset show that our method outperforms state-of-the-art baselinemodels without heavy engineering.

Incorporating Textual Evidence in Visual Storytelling