PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912239697526784 |
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| author | Zheng, Hao Guan, Xinyan Kong, Hao Zheng, Jia Zhou, Weixiang Lin, Hongyu Lu, Yaojie He, Ben Han, Xianpei Sun, Le |
| author_facet | Zheng, Hao Guan, Xinyan Kong, Hao Zheng, Jia Zhou, Weixiang Lin, Hongyu Lu, Yaojie He, Ben Han, Xianpei Sun, Le |
| contents | Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, overlooking visual appeal and structural coherence, which limits their practical applicability. To address these limitations, we propose PPTAgent, which comprehensively improves presentation generation through a two-stage, edit-based approach inspired by human workflows. PPTAgent first analyzes reference presentations to extract slide-level functional types and content schemas, then drafts an outline and iteratively generates editing actions based on selected reference slides to create new slides. To comprehensively evaluate the quality of generated presentations, we further introduce PPTEval, an evaluation framework that assesses presentations across three dimensions: Content, Design, and Coherence. Results demonstrate that PPTAgent significantly outperforms existing automatic presentation generation methods across all three dimensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides Zheng, Hao Guan, Xinyan Kong, Hao Zheng, Jia Zhou, Weixiang Lin, Hongyu Lu, Yaojie He, Ben Han, Xianpei Sun, Le Artificial Intelligence Computation and Language Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, overlooking visual appeal and structural coherence, which limits their practical applicability. To address these limitations, we propose PPTAgent, which comprehensively improves presentation generation through a two-stage, edit-based approach inspired by human workflows. PPTAgent first analyzes reference presentations to extract slide-level functional types and content schemas, then drafts an outline and iteratively generates editing actions based on selected reference slides to create new slides. To comprehensively evaluate the quality of generated presentations, we further introduce PPTEval, an evaluation framework that assesses presentations across three dimensions: Content, Design, and Coherence. Results demonstrate that PPTAgent significantly outperforms existing automatic presentation generation methods across all three dimensions. |
| title | PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2501.03936 |