Papers
arxiv:2501.03936

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

Published on Jan 7
· Submitted by Forceless on Jan 8
Authors:
,
,

Abstract

Automatically generating presentations from documents is a challenging task that requires balancing content quality, visual design, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, often overlooking visual design 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 understand their structural patterns and content schemas, then drafts outlines and generates slides through code actions to ensure consistency and alignment. 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. Experiments show that PPTAgent significantly outperforms traditional automatic presentation generation methods across all three dimensions. The code and data are available at https://github.com/icip-cas/PPTAgent.

Community

Paper author Paper submitter

Hi, Everyone
We proposed PPTAgent, a system for automatically generating presentations from documents. It follows a two-step process inspired by how people create slides, ensuring high-quality content, clear structure, and visually appealing design. To evaluate the generated presentations, we also introduce PPTEval, a framework that measures the quality of presentations in terms of content, design, and coherence.
Github Link, Dataset

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

The following papers were recommended by the Semantic Scholar API

Please give a thumbs up to this comment if you found it helpful!

If you want recommendations for any Paper on Hugging Face checkout this Space

You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend

Paper author Paper submitter

news:
We've released our code of the workflow and UI at https://github.com/icip-cas/PPTAgent
Dataset: https://huggingface.co/datasets/Forceless/Zenodo10K

Sign up or log in to comment

Models citing this paper 0

No model linking this paper

Cite arxiv.org/abs/2501.03936 in a model README.md to link it from this page.

Datasets citing this paper 1

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2501.03936 in a Space README.md to link it from this page.

Collections including this paper 3