Work

Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions

Year: 2026

Type: conference-paper

Abstract: Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains laborintensive.Existing automation methods mostly follow fixed template filling and cannot support dynamic updates for diverse, user-authored slide decks.We therefore define "Dynamic Slide Update via Natural Language Instructions on User-provided Templates" and introduce DynaSlide, a large-scale benchmark with 20,036 real-world instruction-execution triples (source slide, user instruction, target slide) grounded in a shared external database and built from business reporting slides under bringyour-own-template (BYO-template) conditions.To tackle this task, we propose SlideAgent, an agent-based framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions.SlideAgent updates content while preserving layout and style, providing a strong reference baseline on DynaSlide.We further design endto-end and component-level evaluation protocols that reveal key challenges and opportunities for future research.The dataset and code are available at https://github.com/XiaoZ hou2024/SlideAgent.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.909

Authors: Kun Zhou, Jiakai He, Wenmian Yang, Zhensheng Wang, Yiquan Zhang, Weijia Jia

Institutions: Beijing Normal University, Beijing Normal University, Zhuhai, Beijing Normal-Hong Kong Baptist University

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Hong Kong Baptist University, National Natural Science Foundation of China, Beijing Normal University

Award: G8160964939