# Neural Content Extraction for Poster Generation of Scientific Papers

arXiv: https://arxiv.org/abs/2112.08550 · HTML: https://mlsanity.com/paper/2112.08550/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019)
- [DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents](https://mlsanity.com/paper/2101.11796/) (2021)
- [Neural Document Summarization by Jointly Learning to Score and Select Sentences](https://mlsanity.com/paper/1807.02305/) (2018)
- [Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models](https://mlsanity.com/paper/1702.06228/) (2017)
- [Self-Supervised Multimodal Opinion Summarization](https://mlsanity.com/paper/2105.13135/) (2021)
- [Learning to Generate Posters of Scientific Papers](https://mlsanity.com/paper/1604.01219/) (2016)

## Built on by (most cited first, 9 of 9)

- [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)
- [SlideTailor: Personalized Presentation Slide Generation for Scientific Papers](https://mlsanity.com/paper/2512.20292/) (2025)
- [PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation](https://mlsanity.com/paper/2508.21720/) (2025)
- [Knowledge-Centric Templatic Views of Documents](https://mlsanity.com/paper/2401.06945/) (2024)
- [PostDoc: Generating Poster from a Long Multimodal Document Using Deep Submodular Optimization](https://mlsanity.com/paper/2405.20213/) (2024)
- [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)
- [AINL-Eval 2025 Shared Task: Detection of AI-Generated Scientific Abstracts in Russian](https://mlsanity.com/paper/2508.09622/) (2025)

## Abstract (arXiv)

The problem of poster generation for scientific papers is under-investigated. Posters often present the most important information of papers, and the task can be considered as a special form of document summarization. Previous studies focus mainly on poster layout and panel composition, while neglecting the importance of content extraction. Besides, their datasets are not publicly available, which hinders further research. In this paper, we construct a benchmark dataset from scratch for this task. Then we propose a three-step framework to tackle this task and focus on the content extraction step in this study. To get both textual and visual elements of a poster panel, a neural extractive model is proposed to extract text, figures and tables of a paper section simultaneously. We conduct experiments on the dataset and also perform ablation study. Results demonstrate the efficacy of our proposed model. The dataset and code will be released.