# Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

arXiv: https://arxiv.org/abs/1702.06228 · HTML: https://mlsanity.com/paper/1702.06228/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2017.

## Built on by (most cited first, 18 of 18)

- [Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers](https://mlsanity.com/paper/2505.21497/) (2025)
- [AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation](https://mlsanity.com/paper/2308.01095/) (2023)
- [AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations](https://mlsanity.com/paper/2602.03828/) (2026)
- [P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark](https://mlsanity.com/paper/2505.17104/) (2025)
- [SciPostLayout: A Dataset for Layout Analysis and Layout Generation of Scientific Posters](https://mlsanity.com/paper/2407.19787/) (2024)
- [Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation](https://mlsanity.com/paper/2112.14699/) (2021)
- [PosterGen: Aesthetic-Aware Multi-Modal Paper-to-Poster Generation via Multi-Agent LLMs](https://mlsanity.com/paper/2508.17188/) (2025)
- [Neural Content Extraction for Poster Generation of Scientific Papers](https://mlsanity.com/paper/2112.08550/) (2021)
- [EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection](https://mlsanity.com/paper/2603.00155/) (2026)
- [PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation](https://mlsanity.com/paper/2508.21720/) (2025)
- [PostDoc: Generating Poster from a Long Multimodal Document Using Deep Submodular Optimization](https://mlsanity.com/paper/2405.20213/) (2024)
- [Any2Poster: Any-Source Poster Generation Across Modalities and Domains](https://mlsanity.com/paper/2606.02915/) (2026)
- [SciPostGen: Bridging the Gap between Scientific Papers and Poster Layouts](https://mlsanity.com/paper/2511.22490/) (2025)
- [SciGA: A Comprehensive Dataset for Designing Graphical Abstracts in Academic Papers](https://mlsanity.com/paper/2507.02212/) (2025)
- [Paper2Web: Let's Make Your Paper Alive!](https://mlsanity.com/paper/2510.15842/) (2025)
- [SciPostLayoutTree: A Dataset for Structural Analysis of Scientific Posters](https://mlsanity.com/paper/2511.18329/) (2025)
- [PosterMELD: Multi-Agent Paper-to-Poster Generation for Controllable Design Diversity with Editable Print-Ready Outputs](https://mlsanity.com/paper/2608.02218/) (2026)
- [From Transient Prompts to Persistent Control: Scientific Poster Generation via Recursive Semantic-Geometric Contracts](https://mlsanity.com/paper/2609.17326/) (2026)

## Abstract (arXiv)

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.