Textual-to-Visual Iterative Self-Verification for Slide Generation

• URL: http://arxiv.org/abs/2502.15412v1
• Date: Fri, 21 Feb 2025 12:21:09 GMT
• Title: Textual-to-Visual Iterative Self-Verification for Slide Generation
• Authors: Yunqing Xu, Xinbei Ma, Jiyang Qiu, Hai Zhao,
• Abstract summary: We decompose the task of generating missing presentation slides into two key components: content generation and layout generation.<n>Our approach significantly outperforms baseline methods in terms of alignment, logical flow, visual appeal, and readability.
• Score: 46.99825956909532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating presentation slides is a time-consuming task that urgently requires automation. Due to their limited flexibility and lack of automated refinement mechanisms, existing autonomous LLM-based agents face constraints in real-world applicability. We decompose the task of generating missing presentation slides into two key components: content generation and layout generation, aligning with the typical process of creating academic slides. First, we introduce a content generation approach that enhances coherence and relevance by incorporating context from surrounding slides and leveraging section retrieval strategies. For layout generation, we propose a textual-to-visual self-verification process using a LLM-based Reviewer + Refiner workflow, transforming complex textual layouts into intuitive visual formats. This modality transformation simplifies the task, enabling accurate and human-like review and refinement. Experiments show that our approach significantly outperforms baseline methods in terms of alignment, logical flow, visual appeal, and readability.

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