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Enhancing Presentation Slide Generation by LLMs with a Multi-Staged End-to-End Approach
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Generating presentation slides from a long document with multimodal elements such as text and images is an important task. This is time consuming and needs domain expertise if done manually. Existing approaches for generating a rich presentation from a document are often semi-automatic or only put a flat summary into the slides ignoring the importance of a good narrative. In this paper, we address this research gap by proposing a multi-staged end-to-end model which uses a combination of LLM and VLM. We have experimentally shown that compared to applying LLMs directly with state-of-the-art prompting, our proposed multi-staged solution is better in terms of automated metrics and human evaluation.
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Cited by 1 Pith paper
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PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides
PPTAgent generates presentations by analyzing reference decks and applying LLM-generated edit actions, and PPTEval provides an MLLM-based score for content, design, and coherence.
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