Transforming Notebooks into Slides: Introducing Slide4N

Transforming Notebooks into Slides: Introducing Slide4N

Table of Contents

1. Introduction
2. Current Challenges in Creating Presentation Slides from Computational Notebooks
   • Complexity of data science work
   • Communication and storytelling
   • Tedious process of transferring content between mediums
3. Introducing Slide4N: A Human-AI Collaborative Approach
   • Analytical pipeline powered by NLP techniques
   • Slide4N: A collaborative tool within JupyterLab
   • Value and potential of Slide4N
4. Design Goals and Mapping to the Pipeline
5. Slide4N User Interface
   • Notebook Overview
   • Control Panel
   • Navigation View
   • Slides Panel
6. Creating Slides with Slide4N: A Live Demo
7. Simplifying Code with Slide4N
   • Generating multiple title candidates
   • Customizing bullet points
   • Rendering cell outputs
8. The Process Behind Slide4N
   • Grouping Relevant cells
   • Organizing bullet points and cell outputs
   • Aligning text and plots
   • Mapping backbones to layout Patterns
9. Evaluating Slide4N: User Study Results
10. Feedback and Future Directions
    • Adaptive slide generation
    • Enhancing data provenance for slides
    • Improving Human-AI collaboration
11. Conclusion and Acknowledgements
12. References

Article

Creating Presentation Slides from Computational Notebooks: Introducing Slide4N, a Human-AI Collaborative Approach

In today's data-driven world, computational notebooks have become a key tool for data exploration and analysis in the field of data science. However, as the complexity of data science work increases, so does the need for effective communication and presentation of findings. Data scientists often find themselves translating their notebook codes and outcomes into storytelling to present to stakeholders and managers. This process of creating presentation slides from notebooks can be tedious and repetitive, requiring the manual distillation of essential information and arranging text and visuals in a narrative format.

To address these challenges, we present Slide4N, a human-AI collaborative approach to augment the slide creation process. Slide4N is built within JupyterLab and is powered by recent NLP techniques. It offers an analytical pipeline that enables interactive and iterative slide creation from computational notebooks. The tool assists data scientists in locating relevant cells, generating useful slide titles and bullet points, and arranging them with a suitable layout. Slide4N also supports further refinement of the generated slides, providing a seamless experience for data scientists.

To demonstrate the functionality of Slide4N, we provide a live demo where Crystal, a data scientist at a real-estate startup, uses the tool to Create a deck of presentation slides. With Slide4N, Crystal quickly generates slides by leveraging the AI-powered capabilities of the tool. She can easily browse through her notebook, select relevant cells, and configure the automatic slide generation process. Slide4N intelligently merges cells Based on relevance and provides different levels of Detail for bullet points, allowing Crystal to customize the content according to her needs. The tool also renders cell outputs, such as plots and tables, on the slides with a Meaningful layout.

In a user study involving 18 data scientists, Slide4N received positive feedback. Participants found the tool easy to learn and use and expressed satisfaction with the final slides generated. They appreciated the collaborative nature of Slide4N, which allowed them to have more control over the slide creation process and incorporate their own ideas seamlessly. The study also provided insights for the future design of similar tools, emphasizing the need for adaptability in slide generation, improved data provenance, and enhanced Human-AI collaboration.

In conclusion, Slide4N offers a promising solution to the challenges faced by data scientists in creating presentation slides from computational notebooks. By combining the power of AI with human expertise, Slide4N simplifies the process, enhances communication, and enables data scientists to effectively convey their findings to different audiences. As the field of data science continues to evolve, the collaboration between humans and AI Tools like Slide4N will play a crucial role in streamlining and improving the presentation of complex data analysis.

Pros:

• Seamlessly integrates with JupyterLab and computational notebooks
• AI-powered capabilities facilitate quick and efficient slide creation
• Customizable options for generating titles, bullet points, and layouts
• Supports refinement of generated slides based on user preferences
• Simplifies the process of translating code into storytelling for presentations

Cons:

• Requires familiarity with JupyterLab and computational notebooks
• May have a learning curve for users new to AI-powered tools
• Limited to creating slides from computational notebooks (not applicable to other types of presentations)

Highlights

• Slide4N: A human-AI collaborative tool for creating presentation slides from computational notebooks
• Analytical pipeline powered by recent NLP techniques to assist data scientists in slide creation
• Seamless integration with JupyterLab and customizable options for generating slide content and layout
• User study shows satisfaction with Slide4N's ease of use and effectiveness in creating slides
• Future directions include adaptability, data provenance enhancement, and improved Human-AI collaboration

FAQ

Q: Can Slide4N be used with other presentation tools besides PowerPoint? A: Slide4N was designed specifically for creating slides from computational notebooks and is currently integrated with JupyterLab. While it may be possible to use Slide4N in conjunction with other presentation tools, its full functionality is optimized for PowerPoint.

Q: How does Slide4N generate slide titles? A: Slide4N uses various methods to generate multiple title candidates for each slide. These methods include extracting topics related to the main stages of the data science workflow, utilizing markdown cells, and employing neural network models. The multiple title candidates provide users with options and ensure more accurate and relevant titles.

Q: Can users customize the generated bullet points in Slide4N? A: Yes, Slide4N allows users to customize the generated bullet points. It provides options for auto-merging cells based on relevance, grouping and concatenating them. Users can also adjust the level of details in the generated points based on their preferences, offering more control over the content of the slides.

Q: Does Slide4N support the rendering of visual elements on the slides? A: Yes, Slide4N captures cell outputs such as plots and tables and renders them on the slides with a meaningful layout. This ensures that the visual elements of the notebooks are accurately represented in the final presentation slides.

Q: What are the future plans for Slide4N? A: The future design of Slide4N and similar tools aims to enhance adaptability in slide generation, improve data provenance for slides, and further enhance Human-AI collaboration. This includes providing different types of content and layouts, better binding of slides to cells for easy modification, and recommending relevant visualizations for improved presentation efficiency.