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

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arxiv 2501.03936 v3 pith:BFDN4W6J submitted 2025-01-07 cs.AI cs.CL

classification cs.AIcs.CL
keywords presentationscontentpptagentcoherencequalityacrossappealcomprehensively
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

    cs.AI 2025-05 conditional novelty 7.0 of 10

    AgentIF introduces a realistic, long-form instruction-following benchmark for agentic scenarios and shows that current LLMs follow fewer than 30% of such instructions perfectly.

  2. OmniPresent: Generating Coherent Presentation Suites from Scientific Papers

    cs.SE 2026-07 conditional novelty 6.5 of 10

    A multi-agent HTML pipeline with shared knowledge and cross-artifact verify-and-repair generates coherent poster/slides/video/page suites from papers and beats specialized baselines on OmniPreBench.

  3. ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A five-skill agent pipeline with one shared paper extractor and hard render gates produces editable posters, videos, and bilingual blogs, leading the Paper2Poster benchmark on aesthetics.

  4. PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation

    cs.AI 2025-08 unverdicted novelty 6.0 of 10

    PosterForest uses a Poster Tree intermediate representation and hierarchical multi-agent reasoning to generate coherent scientific posters without training, outperforming prior methods in evaluations.

  5. AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?

    cs.DB 2025-08 unverdicted novelty 6.0 of 10

    A benchmark organized by a six-type taxonomy of ambiguous graph queries reportedly shows that nine LLMs, including top models, frequently produce wrong query translations.

  6. PresentAgent: Multimodal Agent for Presentation Video Generation

    cs.CV 2025-07 reject novelty 5.0 of 10

    PresentAgent chains LLM segmentation, slide rendering, TTS, and ffmpeg to turn documents into narrated presentation videos, but the human-level claim rests on five documents and an unvalidated VLM judge.

  7. SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design

    cs.CV 2025-06 conditional novelty 5.0 of 10

    SlideCoder converts slide design images to editable python-pptx code and reports large gains over prior baselines on a new difficulty-tiered benchmark.

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