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Enhancing Presentation Slide Generation by LLMs with a Multi-Staged End-to-End Approach

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arxiv 2406.06556 v1 pith:KBKL6YCN submitted 2024-06-01 cs.CL cs.AI

classification cs.CLcs.AI
keywords multi-stagedpresentationdocumentend-to-endgeneratingllmsslidesaddress
verification ladder T0 review T1 audit T2 compute T3 formal
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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

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides

    cs.AI 2025-01 conditional novelty 6.0 of 10

    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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