PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation
Xin-Sheng Chen, Jiayu Zhu, Pei-lin Li, Hanzheng Wang, Shuojin Yang, Meng-Hao Guo

TL;DR
PresentBench introduces a detailed, rubric-based benchmark with 238 instances and 54 checklist items per instance to evaluate automated slide generation, enabling more precise assessment aligned with human preferences.
Contribution
This paper presents PresentBench, a novel fine-grained, rubric-based benchmark for evaluating slide generation, addressing the limitations of coarse-grained assessments and improving evaluation reliability.
Findings
PresentBench provides more reliable evaluation results than existing methods.
It exhibits significantly stronger alignment with human preferences.
NotebookLM outperforms other slide generation methods.
Abstract
Slides serve as a critical medium for conveying information in presentation-oriented scenarios such as academia, education, and business. Despite their importance, creating high-quality slide decks remains time-consuming and cognitively demanding. Recent advances in generative models, such as Nano Banana Pro, have made automated slide generation increasingly feasible. However, existing evaluations of slide generation are often coarse-grained and rely on holistic judgments, making it difficult to accurately assess model capabilities or track meaningful advances in the field. In practice, the lack of fine-grained, verifiable evaluation criteria poses a critical bottleneck for both research and real-world deployment. In this paper, we propose PresentBench, a fine-grained, rubric-based benchmark for evaluating automated real-world slide generation. It contains 238 evaluation instances, each…
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Taxonomy
TopicsData Visualization and Analytics · Interactive and Immersive Displays · Video Analysis and Summarization
