Title: Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models,
Abstract: Researchers often summarize their work in the form of scientific posters.
Posters provide a coherent and efficient way to convey core ideas expressed in
scientific papers. Generating a good scientific poster, however, is a complex
and time consuming cognitive task, since such posters need to be readable,
informative, and visually aesthetic. In this paper, for the first time, we
study the challenging problem of learning to generate posters from scientific
papers. To this end, a data-driven framework, that utilizes graphical models,
is proposed. Specifically, given content to display, the key elements of a good
poster, including attributes of each panel and arrangements of graphical
elements are learned and inferred from data. During the inference stage, an MAP
inference framework is employed to incorporate some design principles. In order
to bridge the gap between panel attributes and the composition within each
panel, we also propose a recursive page splitting algorithm to generate the
panel layout for a poster. To learn and validate our model, we collect and
release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which
consists of scientific papers and corresponding posters with exhaustively
labelled panels and attributes. Qualitative and quantitative results indicate
the effectiveness of our approach.