Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

TL;DR

This paper introduces a data-driven, probabilistic graphical model framework for automatically generating scientific posters from papers, capturing layout and content attributes to produce readable and aesthetic posters.

Contribution

It presents the first model that learns poster layout and content attributes from data, using graphical models and a recursive page splitting algorithm, validated on a new benchmark dataset.

Findings

Effective poster generation demonstrated through qualitative results.

Quantitative metrics show improved layout coherence.

The dataset enables future research in automatic poster creation.

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.