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

Review of the paper: Enhancing Presentation Slide Generation by LLMs with a Multi-Staged End-to-End Approach

1. Context and Problem to Solve

Context

Creating presentations is an essential part of communicating ideas effectively in fields such as education, research, and business. These presentations often need to be visually appealing, concise, and structured in a way that tells a coherent story. However, generating slides from lengthy documents containing both text and images is time-consuming and typically requires domain expertise.

Automating this process is challenging because:

1. Presentations need to convey narratives, not just summaries. This means the flow of information should be engaging and logical.

2. Text and images must work together to explain ideas clearly.

3. Large Language Models (LLMs) face limitations when processing long documents due to context-length constraints. They also struggle with accurately linking generated slides to specific sections in the source document.

4. Existing approaches either rely on semi-automated tools or lack the ability to create meaningful narratives.

Problem Statement

How can we develop a method to automatically generate presentation slides from long, multimodal documents that:

• Captures the main ideas and structure of the document.

• Uses images effectively.

• Maintains a logical flow of information.

• Minimizes errors, such as hallucinations (incorrect or fabricated content)?

2. Methods Used in the Study

Overview of the Proposed Approach

The authors introduce DocPres, a multi-staged, end-to-end framework designed to generate slides from long documents. It breaks down the task into smaller, more manageable steps:

1. Extracting Content:

   • Text and images are extracted hierarchically from the document using an API.

2. Document Summarization (Bird's-eye View):

   • The document is summarized hierarchically, starting from sub-sections to sections and finally the entire document.

3. Outline Generation:

   • Titles for slides are generated using chain-of-thought prompts to ensure logical flow.

4. Mapping Sections to Slides:

   • Sections and subsections are mapped to slide titles, ensuring consistency and reducing hallucinations.

5. Slide Content Generation:

   • Text content is generated slide-by-slide, considering the content of previous slides to maintain flow.

6. Image Selection:

   • Relevant images are selected using a similarity score between image embeddings and text embeddings, ensuring high-quality visual support.

Key Tools and Models

• LLMs (Large Language Models): Used for text summarization and slide generation (e.g., GPT-3.5-turbo).

• VLMs (Vision-Language Models): Used for image processing and selection (e.g., CLIP model).

• Cosine Similarity: Measures relevance between text and images.

• Edit Distance: Ensures accurate mapping of slide titles to document sections.

3. Key Results

Experimental Setup

The method was tested on SciDuet, a dataset containing 100 academic papers, and compared against four baselines:

1. D2S: Semi-automatic method with pre-defined slide titles.

2. GPT-Flat: Uses GPT-3.5 with a simple descriptive prompt.

3. GPT-COT: Incorporates chain-of-thought prompting.

4. GPT-Cons: Adds constraints for slide length and content.

Metrics Used

1. Coverage: Measures how well the generated slides reflect the source document.

2. Perplexity (PPL): Evaluates grammatical correctness.

3. LLM-Eval: Assesses presentation quality based on clarity, coherence, and structure.

Key Findings

• Coverage: DocPres achieved the highest scores, covering 39.13% at the paragraph level and 24.73% at the sentence level.

• Perplexity: DocPres had the lowest perplexity (58.01), indicating better grammatical correctness.

• LLM-Eval: Scored 8.95/10, showing high-quality outputs comparable to human standards.

• Human Evaluation: Experts rated DocPres higher than baselines for readability (3.9/5), consistency (3.8/5), and usability (3.2/5).

4. Conclusions and Implications

Main Takeaways

1. Effectiveness of Multi-Staged Approach: Dividing the task into smaller subtasks significantly improved performance compared to directly prompting LLMs.

2. Balanced Use of Text and Images: Combining text summaries with relevant visuals enhanced the narrative quality of the slides.

3. Scalability and Usability: DocPres can process long documents effectively without requiring training data.

Practical Implications

• Academics and Researchers: Save time when preparing presentations for conferences.

• Businesses: Streamline the creation of marketing decks and reports.

• Educational Tools: Support teachers in quickly summarizing textbooks and research papers.

Limitations and Future Work

1. Image Handling: Struggles with complex, non-natural images like flowcharts and graphs.

2. Computational Cost: Frequent use of LLMs may require optimization.

3. Single Document Focus: Cannot yet merge content from multiple sources.