UniPPTBench: A Unified Benchmark for Presentation Generation Across Diverse Input Settings
Bo Zhao, Maosheng Pang, Chen Zhang, Huan Yang, Yixin Cao, Wei Ji

TL;DR
UniPPTBench introduces a comprehensive benchmark and evaluation framework for presentation generation across diverse real-world input scenarios, addressing current limitations in scenario-specific assessment.
Contribution
It provides a unified benchmark and scenario-aware evaluation protocol for presentation generation, covering multiple input settings and supporting reproducible comparisons.
Findings
Significant performance variation across different input settings.
Common failure modes include issues in content grounding and multimodal integration.
High scores on generic metrics do not always indicate task-specific success.
Abstract
Existing works typically focus on presentation generation under isolated input settings, whereas real-world use cases span diverse scenarios, including vague user prompts, long documents, multimodal materials, and multiple heterogeneous sources. Moreover, current evaluations are often insufficiently scenario-specific. They mainly rely on generic presentation-quality criteria, such as visual appeal, layout quality, and overall coherence, but fail to assess the core capabilities required by different input settings, including grounded compression, visual-text alignment, and cross-source synthesis. Consequently, the field lacks a unified benchmark and a scenario-aware evaluation framework for faithfully diagnosing presentation-generation systems across diverse real-world settings. We present UniPPTBench, a unified benchmark for presentation generation across four representative input…
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