Pith. sign in

REVIEW 9 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

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

classification cs.AI cs.CL
keywords presentationscontentpptagentcoherencequalityacrossappealcomprehensively
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 9 Pith papers

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

  1. Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning

    cs.CV 2026-01 conditional novelty 7.0

    VIGA introduces a training-free interleaved multimodal reasoning loop that improves vision-as-inverse-graphics accuracy over one-shot baselines on BlenderGym, SlideBench, and new BlenderBench.

  2. SciGA: A Comprehensive Dataset for Designing Graphical Abstracts in Academic Papers

    cs.CV 2025-07 unverdicted novelty 7.0

    Introduces the SciGA-145k dataset with intra-paper and cross-paper graphical abstract recommendation tasks plus the CAR evaluation metric.

  3. OmniPresent: Generating Coherent Presentation Suites from Scientific Papers

    cs.SE 2026-07 conditional novelty 6.5

    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.

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

    cs.CV 2026-07 conditional novelty 6.0

    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.

  5. Any2Poster: Any-Source Poster Generation Across Modalities and Domains

    cs.CV 2026-06 unverdicted novelty 6.0

    Any2Poster Bench tests poster generation from 8 modalities and 5 domains using quizzes and VLM judgments; Any2Poster Agent reaches 87% accuracy and beats prior paper-only methods.

  6. Crafter: A Multi-Agent Harness for Editable Scientific Figure Generation from Diverse Inputs

    cs.CV 2026-05 unverdicted novelty 6.0

    Crafter introduces a multi-agent harness for generating and editing scientific figures across types and inputs, with a new benchmark showing outperformance over baselines.

  7. VideoAgent: Personalized Synthesis of Scientific Videos

    cs.AI 2025-09 unverdicted novelty 6.0

    VideoAgent is a modular framework that redefines scientific video synthesis as an intent-driven planning problem and introduces the SciVidEval benchmark for multimodal quality and pedagogical utility.

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

    cs.AI 2025-08 unverdicted novelty 6.0

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

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

    cs.CV 2026-07 conditional novelty 5.0

    A five-skill agent pipeline generates an editable poster, video deck, and bilingual blog from a paper PDF, binds them in an interactive viewer, and reports poster scores above the authors' own under two VLM judges.