ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback

Abstract:Automatic academic paper-to-slide generation is inherently iterative, because creating an effective presentation requires repeated cycles of generation, critique, and revision. Recent multi-agent systems partially acknowledge this through internal critique-and-revise loops, while conversational approaches allow users to refine generated slide decks through dialog. However, these refinement processes either remain largely closed to the user or introduce feedback only after a complete deck has been produced, limiting the user's ability to participate in the iterative refinement of narrative flow, content allocation, and presentation emphasis. To address this gap, we introduce ConvDeck, a multi-agent pipeline for conversational paper-to-slide generation that distributes interaction across the pipeline through stage-specific loops, allowing users to iteratively refine both the presentation outline and the final slide deck at the stages where each kind of decision is made. These loops are driven by a refinement mechanism in which agents can think, speak, and act, enabling them to either directly apply edits or respond conversationally to clarify user feedback and discuss revision options. Our evaluation shows that stage-specific conversational feedback improves user-goal satisfaction while preserving narrative coherence, content quality, and visual presentation.

Accepted to Findings of EMNLP 2026. Project webpage: this https URL

Artificial Intelligence (cs.AI)

arXiv:2609.00226 [cs.AI]

(or arXiv:2609.00226v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.00226 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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