cs.AIApr 13, 2026
SaveNarrative-Driven Paper-to-Slide Generation via ArcDeck
Organizations: University of Illinois Urbana-Champaign · Middle East Technical University
Abstract
We introduce ArcDeck, a multi-agent framework that formulates paper-to-slide generation as a structured narrative reconstruction task. Unlike existing methods that directly summarize raw text into slides, ArcDeck explicitly models the source paper's logical flow. It first parses the input to construct a discourse tree and establish a global commitment document, ensuring the high-level intent is preserved. These structural priors then guide an iterative multi-agent refinement process, where specialized agents iteratively critique and revise the presentation outline before rendering the final visual layouts and designs. To evaluate our approach, we also introduce ArcBench, a newly curated benchmark of academic paper-slide pairs. Experimental results demonstrate that explicit discourse modeling, combined with role-specific agent coordination, significantly improves the narrative flow and logical coherence of the generated presentations.
Figures & tables
Figure 1: Framework Comparison. (a) Prior methods rely on raw-text summarization , isolated section-level organization , or direct outline extraction , yielding condensed text without narrative guidance. (b) In contrast, ArcDeck explicitly models discourse structure and establishes global commitments to produce narrative-driven outlines through an iterative multi-agent refinement loop.
Figure 2: ArcDeck Overview. 1) Preprocessing extracts textual and visual assets from the source paper; 2) Narrative-Driven Outline Generation combines discourse parsing and global commitment to guide the Narrative Refinement Loop , producing a structured Slide Outline; and 3) Slide Generation constructs the initial deck and refines its aesthetic layout to yield the final presentation. The numbered indices of each agent, e.g . , 1 , denote their temporal execution order, while the icons at the bottom of each agent box indicate the specific structural inputs they receive.
| Relation | Explanation | Type |
| Elaboration | Adds detail, examples, or implementation specifics. | NS |
| Explanation | Clarifies why or how a nucleus claim holds. | NS |
| Context | Background or definitions needed to interpret the nucleus. | NS |
| Purpose | Goal or motivation associated with the nucleus. | NS |
| Evaluation | Assessment, strength, or limitation of the nucleus. | NS |
| Organization | Roadmap or meta-structural framing. | NS |
Table 1: RST Relation Taxonomy. Definitions of the rhetorical relations used to construct our discourse trees (NS: Nucleus-Satellite; MN: Multinuclear).
Figure 3: Example Discourse Trees from Secs. 1 & 3 of this paper. Leaf nodes correspond to elementary discourse units (EDUs, corresponding to paragraphs), while internal nodes and arrows denote rhetorical relations that recursively group these spans.
Figure 4: Global Commitment Example. The global commitment establishes the high-level narrative progression of the presentation through five key components: a snapshot, core content, a talk contract, a narrative spine, and a light section plan.
Figure 5: Slide Generation Stage. Slide Deck Constructor 6 handles asset mapping, layout selection, and text generation, while the Aesthetics Refiner 7 polishes the deck via figure matching and content formatting.
Figure 6: ArcBench Statistics. Overview of our curated 100-pair dataset, showing the distribution of research topics across six major CV & ML venues, publication years, visual density, and author-prepared presentation lengths.
| Category | Specific Metric | Input | Output | High-Level Idea |
| Story | Pres. Text & 25 MCQs | Storyline capture and high-level information retention. | ||
| Visuals | Slide Images & 25 MCQs | Information conveyed through visual elements and slide layout. | ||
| Hard | Pres. Text & 25 MCQs | Conceptual understanding of methodology and limitations. | ||
| VLM-based Q/A Quiz | Depth | Pres. Text & 25 MCQs | Accuracy (%) | Coverage of important technical/implementation details. |
| Text Quality (TQ) | Technical substance and specificity of the text. | |||
| Narrative Flow (NF) | Pres. Text | Logical progression and comprehensive section coverage. |
Table 2: Summary of Evaluation Metrics
| VLM-based Q/A Quiz (Open) | VLM-based Q/A Quiz (Closed) | VLM-as-Judge (Open) | VLM-as-Judge (Closed) | |||||||||||||
| Method | Story | Visuals | Hard | Depth | Story | Visuals | Hard | Depth | TQ | NF | VL | VT | TQ | NF | VL | VT |
| Generation Model: GPT-5 | ||||||||||||||||
| HTML | 95.16 | 64.13 | 81.83 | 69.73 | 95.60 | 38.40 | 80.00 | 82.20 | 86.11 | 89.06 | 95.22 | 76.77 | 58.50 | 61.50 | 63.67 | 10.00 |
| P2Poster | 95.22 | 65.51 | 76.70 | 67.67 | 87.00 | 27.00 | 66.40 | 64.60 | 62.20 | 70.55 | 97.08 | 91.35 | 35.17 | 41.67 | 72.00 | 78.50 |
| PPTAgent | 92.96 | 65.08 | 69.49 | 65.67 | 75.00 | 12.80 | 32.00 | 36.20 | 35.95 | 55.94 | 84.86 | 81.95 | 16.50 | 37.67 | 71.33 | 57.50 |
| SlideGen | 95.26 | 68.37 | 78.39 | 69.43 | 97.60 | 35.80 | 86.00 | 82.40 | 69.02 | 88.18 | 97.36 | 95.28 | 36.17 | 56.67 | 88.33 | 84.83 |
Table 3: VLM-Based Evaluation. The best value in each generation model is highlighted in green , and runner-up is underlined.
Table 4: VLM Pairwise Preference (A/B Test). Holistic evaluation of generated presentations using two comparison protocols: assessing narrative flow against baselines (left), and overall quality against Author-Prepared (AP) slides (right). To mitigate single-model bias, both closed- (GPT-5) and open-source (Qwen3-VL) judges are used.
| Generation Model | GPT-5 | GPT-4o | Qwen3 | |||
| Automated Metrics | R-L | PPL | R-L | PPL | R-L | PPL |
| HTML | 64.7 | 39.90 | 33.6 | 31.51 | 108.5 | 31.08 |
| Paper2Poster | 71.3 | 21.04 | 53.8 | 22.07 | 76.8 | 16.96 |
| PPTAgent | 33.6 | 91.43 | 34.6 | 57.21 | 64.6 | 23.33 |
| SlideGen | 55.1 | 80.46 | 45.2 | 43.84 | 83.9 | 23.09 |
| ArcDeck | 57.1 | 46.01 | 84.8 | 33.47 | 156.0 | 16.95 |
Table 5: Automated Metrics Evaluation. The best value in each column is highlighted in green , and runner-up is underlined. R-L values are scaled by .
Figure 7: Qualitative Comparison. Unlike baselines, ArcDeck shows clear narrative flow instead of mirroring paper section structure and avoids cross-slide content overlap.
Figure 8: Qualitative Ablation Study. Comparison between ArcDeck and variants without the Discourse Parser (DP) or Commitment Builder (CB).
Figure 9: Token Usage Analysis.
Appendix figures & tables60 assets
Supplementary material from the paper’s appendix.
Appendix
| Human Evaluation Topic | ||||
| Method | V-L / MM | Gen. Models | CV Core | Overall |
| PPTAgent | 1.03 | 1.13 | 1.20 | 1.10 |
| SlideGen | 2.42 | 2.23 | 2.14 | 2.30 |
| ArcDeck | 2.55 | 2.63 | 2.66 | 2.60 |
Table 6: Human Evaluation Results. Participants ranked slide decks based on content quality and narrative flow, where Rank 1 = 3 points, Rank 2 = 2 points, and Rank 3 = 1 point. The best value in each topic is highlighted in green , and runner-up is underlined.
Figure 10: Qualitative Comparison of ArcDeck-generated Slides under Different Presentation Durations. 20-minute presentation includes more detailed content, while the 5-minute presentation preserves the key points of the paper.
Figure 11: Qualitative Comparison of ArcDeck-generated Slides under Different Target Audiences. The figure illustrates how ArcDeck adapts content selection, terminology, and level of technical detail according to the specified target audience.
Figure 12: Distribution of Discourse Tree Relations across Section Groups. Stacked bars represent the relative proportion of each discourse relation within each section group.
Figure 13: Relationship of Generated Discourse-Tree Height, Average Leaf Depth, and Group Count per Paragraph to the Number of Paragraphs. Pearson correlation coefficients are shown in the top-left corner of each figure.
| VLM-as-Judge | Pairwise Comparison | |
| Method | NF | ArcDeck Win Rate % |
| ArcDeck w/o Discourse Parser | 7.50 | 89.1 |
| ArcDeck w/o Global Comm. | 7.52 | 94.5 |
| ArcDeck w/o Narr. Ref. Loop | 8.68 | 61.8 |
| ArcDeck | 9.70 | – |
Table 7: Narrative-Driven Outline Generation Ablation Study. Narrative Flow (NF) scores for different ablated versions, along with the full ArcDeck model’s narrative flow pairwise comparison win rate against these ablated versions, are shown.
| VLM-as-Judge | Pairwise Comparison | |
| NRL Iterations # | NF | NF Win Rate vs. 1st It. |
| 1 iteration | 4.18 | – |
| 2 iterations | 7.00 | 56.4 % |
| 3 iterations (Final) | 7.34 | 100 % |
Table 8: Narrative Refinement Loop (NRL) Improvement Study. Narrative Flow (NF) scores for different numbers of iterations, together with the win rate relative to the first iteration, are shown.
Figure 14: Human Evaluation Interface. Participants were shown slide decks generated by different methods and asked to rank them based on content quality and narrative flow.
| Venue | 2022 | 2023 | 2024 | 2025 | Total | ||||||||||
| CVPR | — | — | 52 | — | 1 | 176 | — | 1 | 228 | — | 2 | ||||
| ECCV | — | — | 222 | — | 27 | — | 222 | — | 27 | ||||||
| ICCV | — | — | — | 296 | — | 4 | 296 | — | 4 | ||||||
| ICLR | — | — | 12 | — | — | 51 | — | 27 | 63 | — | 27 | ||||
| ICML | 52 | — | — | 38 | — | 3 | 40 | — | 17 | 22 | — | 8 | 152 | — | 28 |
| NeurIPS | — | 14 | — | 4 | 10 | — | 4 | 9 | — | 4 | 33 | — | 12 | ||
Table 9: ArcBench Venue and Year Distribution. Each cell: pool — bench. Dashes indicate no coverage.
Figure 15: Key Statistics of Papers and Slide Decks in ArcBench . We report distributions for the full paper pool and the curated benchmark, including the number of figures, tables, character counts, and Oral papers.
Figure 16: ArcBench Topic Distribution. We report the distribution of papers across research topics.
| Dataset | Year | Pairs | Venues | Domain | Type | Pub. |
| DOC2PPT | 2022 | 5,873 | General academic | Multi-domain | Oral & Poster | ✓ |
| SciDuet | 2021 | 1,088 | ICML, NeurIPS, ACL | NLP / ML | Oral & Poster | ✓ |
| Zenodo10K | 2025 | 10K | — | Multi-domain | — | ✓ |
| SLIDESBENCH † | 2025 | 7,585 | — | General | — | ✓ |
| SlideGen | 2025 | 200 | ICLR, ICML, NeurIPS | AI / ML | Oral-only | ✗ |
| ArcBench | 2025 | 100 | 6 venues ‡ | CV / ML | Oral-only | ✓ |
Table 10: Comparison of ArcBench with Prior Paper-to-Slide Datasets. † Generates individual slides from NL instructions rather than full papers. ‡ CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS.
Figure 17: Pearson Correlation of Normalized Scores Across Methods. (a,b) Correlation between presentations generated by Qwen3-VL-32B and GPT-5, evaluated by Qwen3 and GPT-5, respectively. (c) Correlation between Qwen3 and GPT-5 evaluator scores across all generated presentations.
Figure 18: ArcDeck Failure Case.
Figure 19: Template Slide Layout. used by ArcDeck
Figure 20: Aesthetic Refiner Ablation. Comparison of slides generated before and after applying the refiner agent.
| VLM-as-Judge | Pairwise Comparison | ||
| Method | VL | VT | Win Rate % |
| Generation Model: GPT-5 | |||
| ArcDeck wo Refiner | 9.62 | 9.37 | 25 |
| ArcDeck | 9.75 | 9.5 | 75 |
Table 11: VLM-as-Judge Evaluation (Open Setting) and Pairwise Visual Aesthetic Comparison (Open Setting). The best value is highlighted in green , and runner-up is underlined.
Figure 21: Qualitative Comparison. Sample generated by ArcDeck -(4o)
Figure 22: Qualitative Comparison. Sample generated by SlideGen-4o
Figure 23: Qualitative Comparison. Sample generated by PPTAgent-4o
Figure 24: Qualitative Comparison. Sample generated by Paper2Poster-4o
Figure 25: Qualitative Comparison. Sample generated by HTML-4o
Figure 26: Additional Qualitative Results. generated by ArcDeck GPT-5 backbone.
Figure 27: Additional Qualitative Results. generated by ArcDeck GPT-5 backbone.
Figure 28: Additional Qualitative Results. generated by ArcDeck GPT4o backbone.
Figure 29: ArcDeck Extension. to JavaScript output format.
Figure 30: ArcDeck Extension. to Latex Beamer output format.
Figure 31: Additional Qualitative Results for diverse domain. Slides generated by ArcDeck (GPT-5 backbone) for a document from the Physics domain.
Figure 32: Additional Qualitative Results for diverse domain. Slides generated by ArcDeck (GPT-5 backbone) for a document from the Biology domain.
Figure 33: Discourse Parser Prompt.
Figure 34: Commitment Builder Prompt.
Figure 35: Slide Planner/Reviser Prompt.
Figure 36: Narrative Critic Prompt.
Figure 37: Narrative Judge Prompt.
Figure 38: Asset Matching Prompt.
Figure 39: Slide Deck Constructor Prompt.
Figure 40: Aesthetic Refiner Prompt.
Figure 41: Pairwise - Narrative Flow Evaluation Prompt.
Figure 42: Pairwise - Overall Quality Evaluation Prompt.
Figure 43: Quiz Generation - Depth Prompt.
Figure 44: Quiz Generation - Hard Prompt.
Figure 45: Quiz Generation - Story Prompt.
Figure 46: Quiz Generation - Visuals Prompt.
Figure 47: Quiz Taker - Text Prompt.
Figure 48: Quiz Taker - Visual Prompt.
Figure 49: VLM as Judge - Narrative Flow Evaluation Prompt.
Figure 50: VLM as Judge - Text Quality Evaluation Prompt.
Figure 51: VLM as Judge - Visual Layout Evaluation Prompt.
Figure 52: VLM as Judge - Visual Thematic Evaluation Prompt.
Figure 53: Example Global Commitment generated for ArcDeck.
Figure 54: Example Global Commitment generated for Attention Is All You Need.
Figure 55: Example full Discourse Parser outputs in JSON format for introduction sections. In (a), the discourse tree for Section 1 of this paper is shown. In (b), the discourse tree for Section 1 of Attention Is All You Need is shown, together with snippets from the corresponding paragraphs.
Figure 56: Example Discourse Parser output in JSON format. Discourse tree for Section 3 (Model Architecture) of Attention Is All You Need, shown together with snippets from the corresponding paragraphs.
Figure 57: Example Slide Outline output in JSON format.
Figure 58: Example Narrative Critic output in JSON format.
Figure 59: Example Failing Narrative Judge output.
Figure 60: Example Passing Narrative Judge output.
Figure 61: Asset Matching Sample Output.
Figure 62: Slide Deck Constructor Sample Output.
Figure 63: Aesthetic Refiner Sample Output.