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PPTAgent, a tool for utilizing LLMs to generate PowerPoint presentations from documents.

Project description

PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides

📄 Paper   |   🤗 OpenSource   |   📝 Documentation   |   Ask DeepWiki DeepWiki   |   🙏 Citation

We present PPTAgent, an innovative system that automatically generates presentations from documents. Drawing inspiration from human presentation creation methods, our system employs a two-step process to ensure excellence in overall quality. Additionally, we introduce PPTEval, a comprehensive evaluation framework that assesses presentations across multiple dimensions.

Open Source 🤗

We have released our model and data at HuggingFace.

Demo Video 🎥

https://github.com/user-attachments/assets/c3935a98-4d2b-4c46-9b36-e7c598d14863

Distinctive Features ✨

  • Dynamic Content Generation: Creates slides with seamlessly integrated text and images
  • Smart Reference Learning: Leverages existing presentations without requiring manual annotation
  • Comprehensive Quality Assessment: Evaluates presentations through multiple quality metrics

Case Study 💡

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PPTAgent 🤖

PPTAgent follows a two-phase approach:

  1. Analysis Phase: Extracts and learns from patterns in reference presentations
  2. Generation Phase: Develops structured outlines and produces visually cohesive slides

Our system's workflow is illustrated below:

PPTAgent Workflow

PPTEval ⚖️

PPTEval evaluates presentations across three dimensions:

  • Content: Check the accuracy and relevance of the slides.
  • Design: Assesses the visual appeal and consistency.
  • Coherence: Ensures the logical flow of ideas.

The workflow of PPTEval is shown below:

PPTEval Workflow

Citation 🙏

If you find this project helpful, please use the following to cite it:

@article{zheng2025pptagent,
  title={PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides},
  author={Zheng, Hao and Guan, Xinyan and Kong, Hao and Zheng, Jia and Zhou, Weixiang and Lin, Hongyu and Lu, Yaojie and He, Ben and Han, Xianpei and Sun, Le},
  journal={arXiv preprint arXiv:2501.03936},
  year={2025}
}

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