PosterSum: A Multimodal Benchmark for Scientific Poster Summarization
Authors: name: Rohit Saxena, name: Pasquale Minervini, name: Frank Keller

Generating accurate and concise textual summaries from multimodal documents
is challenging, especially when dealing with visually complex content like
scientific posters. We introduce PosterSum, a novel benchmark to advance the
development of vision-language models that can understand and summarize
scientific posters into research paper abstracts. Our dataset contains 16,305
conference posters paired with their corresponding abstracts as summaries. Each
poster is provided in image format and presents diverse visual understanding
challenges, such as complex layouts, dense text regions, tables, and figures.
We benchmark state-of-the-art Multimodal Large Language Models (MLLMs) on
PosterSum and demonstrate that they struggle to accurately interpret and
summarize scientific posters. We propose Segment & Summarize, a hierarchical
method that outperforms current MLLMs on automated metrics, achieving a 3.14%
gain in ROUGE-L. This will serve as a starting point for future research on
poster summarization.