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arXiv 2609.00226cs.AI

ConvDeck:通过阶段特定用户反馈实现的对话式论文转幻灯片生成

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

  • University of Illinois Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校)

机构由 AI 辅助整理,请以论文原文为准。

Tarik Can Ozden, Sachidanand VS, Furkan Horoz, Ozgur Kara, Dilek Hakkani-Tür, Junho Kim, James Matthew Rehg

AI总结:

ConvDeck是一种多智能体对话式论文转幻灯片生成流水线,通过阶段特定循环让用户迭代优化演示大纲与幻灯片集,其阶段特定对话反馈可在保障多方面质量的同时提升用户目标满意度。

AI中文摘要:

自动学术论文转幻灯片生成本质上是迭代的,因为创建有效演示文稿需要反复的生成、批评和修订循环。最近的多智能体系统通过内部批评-修订循环部分承认了这一点,而对话式方法允许用户通过对话优化生成的幻灯片集。然而,这些优化过程要么对用户基本封闭,要么仅在完整幻灯片集生成后才引入反馈,限制了用户参与叙事流程、内容分配和演示重点的迭代优化的能力。为解决这一差距,我们提出了ConvDeck,一种用于对话式论文转幻灯片生成的多智能体流水线,它通过阶段特定循环在流水线中分配交互,允许用户在做出每种决策的阶段迭代优化演示文稿大纲和最终幻灯片集。这些循环由一种优化机制驱动,其中智能体可以思考、说话和行动,使它们能够直接应用编辑或进行对话式回应以澄清用户反馈并讨论修订选项。我们的评估表明,阶段特定的对话式反馈在保持叙事连贯性、内容质量和视觉呈现的同时,提高了用户目标满意度。

英文摘要:

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.

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