  Visual storytelling aims to generate a narrative paragraph from a sequence of
images automatically. Existing approaches construct text description
independently for each image and roughly concatenate them as a story, which
leads to the problem of generating semantically incoherent content. In this
paper, we propose a new way for visual storytelling by introducing a topic
description task to detect the global semantic context of an image stream. A
story is then constructed with the guidance of the topic description. In order
to combine the two generation tasks, we propose a multi-agent communication
framework that regards the topic description generator and the story generator
as two agents and learn them simultaneously via iterative updating mechanism.
We validate our approach on VIST dataset, where quantitative results,
ablations, and human evaluation demonstrate our method's good ability in
generating stories with higher quality compared to state-of-the-art methods.
