OutlineSpark: Igniting AI-powered Presentation Slides Creation from Computational Notebooks through Outlines (2403.09121v1)
Abstract: Computational notebooks are widely utilized for exploration and analysis. However, creating slides to communicate analysis results from these notebooks is quite tedious and time-consuming. Researchers have proposed automatic systems for generating slides from notebooks, which, however, often do not consider the process of users conceiving and organizing their messages from massive code cells. Those systems ask users to go directly into the slide creation process, which causes potentially ill-structured slides and burdens in further refinement. Inspired by the common and widely recommended slide creation practice: drafting outlines first and then adding concrete content, we introduce OutlineSpark, an AI-powered slide creation tool that generates slides from a slide outline written by the user. The tool automatically retrieves relevant notebook cells based on the outlines and converts them into slide content. We evaluated OutlineSpark with 12 users. Both the quantitative and qualitative feedback from the participants verify its effectiveness and usability.
- Robert RH Anholt. 2010. Dazzle’em with style: The art of oral scientific presentation. Elsevier, San Diego, California, USA.
- Damian Avila. 2019. RISE. https://github.com/damianavila/RISE
- Outline wizard: presentation composition and search. In Proceedings of the 15th International Conference on Intelligent User Interfaces (Hong Kong, China) (IUI ’10). Association for Computing Machinery, New York, NY, USA, 209–218. https://doi.org/10.1145/1719970.1719999
- Aspirations and Practice of ML Model Documentation: Moving the Needle with Nudging and Traceability. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 749, 17 pages. https://doi.org/10.1145/3544548.3581518
- Comparing attention-based convolutional and recurrent neural networks: Success and limitations in machine reading comprehension. arXiv preprint arXiv:1808.08744 abs/1808.08744 (2018).
- Matthew Brehmer and Robert Kosara. 2021. From jam session to recital: Synchronous communication and collaboration around data in organizations. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2021), 1139–1149.
- John Brooke. 1996. SUS: A Quick and Dirty Usability. Usability evaluation in industry 189, 3 (1996), 189–194.
- What’s Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3313831.3376729
- From analysis to communication: Supporting the lifecycle of a story. In Data-Driven Storytelling. AK Peters/CRC Press, Boca Raton, FL, USA, 151–183.
- David Donoho. 2017. 50 years of data science. Journal of Computational and Graphical Statistics 26, 4 (2017), 745–766.
- Wrex: A Unified Programming-by-Example Interaction for Synthesizing Readable Code for Data Scientists. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3313831.3376442
- Leveraging Analysis History for Improved In Situ Visualization Recommendation. Computer Graphics Forum 41, 3 (2022), 145–155. https://doi.org/10.1111/cgf.14529 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14529
- Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time). In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019. Association for Computational Linguistics, Stroudsburg, PA, USA, 3141–3150.
- Philip Jia Guo. 2012. Software tools to facilitate research programming. Stanford University, Stanford, California, USA.
- Managing messes in computational notebooks. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, Glasgow, Scotland, UK, 1–12.
- Jeffrey Heer. 2019. Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences 116, 6 (2019), 1844–1850.
- Project Jupyter. 2015. Project Jupyter: Computational Narratives as the Engine of Collaborative Data Science. the Helmsley Trust, the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation. https://blog.jupyter.org/project-jupyter-computational-narratives-as-the-engine-ofcollaborative-data-science-2b5fb94c3c58
- Project Jupyter. 2018. JupyterLab: the next generation of the Jupyter Notebook. Project Jupyter. https://blog.jupyter.org/jupyterlab-thenext-generation-of-the-jupyter-notebook-5c949dabea3
- ToonNote: Improving Communication in Computational Notebooks Using Interactive Data Comics. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 727, 14 pages. https://doi.org/10.1145/3411764.3445434
- ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences 103 (2023), 102274.
- Variolite: Supporting Exploratory Programming by Data Scientists. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 1265–1276. https://doi.org/10.1145/3025453.3025626
- Towards Effective Foraging by Data Scientists to Find Past Analysis Choices. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300322
- The story in the notebook: Exploratory data science using a literate programming tool. In Proceedings of the 2018 CHI conference on human factors in computing systems. ACM, New York, United States, 1–11.
- mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (Virtual Event, USA) (UIST ’20). Association for Computing Machinery, New York, NY, USA, 140–151. https://doi.org/10.1145/3379337.3415842
- The emerging role of data scientists on software development teams. In Proceedings of the 38th International Conference on Software Engineering (Austin, Texas) (ICSE ’16). Association for Computing Machinery, New York, NY, USA, 96–107. https://doi.org/10.1145/2884781.2884783
- Jupyter Notebooks-a publishing format for reproducible computational workflows. Vol. 2016. IOS Press, Virginia, USA.
- Sean Kross and Philip Guo. 2021. Orienting, framing, bridging, magic, and counseling: How data scientists navigate the outer loop of client collaborations in industry and academia. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–28.
- Sean Kross and Philip J Guo. 2019. Practitioners teaching data science in industry and academia: Expectations, workflows, and challenges. In Proceedings of the 2019 CHI conference on human factors in computing systems. ACM, New York, USA, 1–14.
- Lux: always-on visualization recommendations for exploratory dataframe workflows. Proc. VLDB Endow. 15, 3 (nov 2021), 727–738. https://doi.org/10.14778/3494124.3494151
- Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling. arXiv:2304.08366 [cs.HC]
- Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 173, 16 pages. https://doi.org/10.1145/3544548.3580965
- NBSearch: Semantic Search and Visual Exploration of Computational Notebooks. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Vol. abs/2102.01275. ACM, New York, USA, 1–14.
- EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In Situ Code Search and Recommendation. ACM Transactions on Interactive Intelligent Systems 13, 1 (2023), 1–27.
- InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks. IEEE Transactions on Visualization and Computer Graphics 30, 1 (2024), 944–954. https://doi.org/10.1109/TVCG.2023.3327170
- Vivian Liu and Lydia B Chilton. 2022. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 384, 23 pages. https://doi.org/10.1145/3491102.3501825
- How data scientistswork together with domain experts in scientific collaborations: To find the right answer or to ask the right question? Proceedings of the ACM on Human-Computer Interaction 3, GROUP (2019), 1–23.
- Microsoft. 2024. Microsoft PowerPoint. Microsoft. https://www.microsoft.com/en-us/microsoft-365/powerpoint
- How data science workers work with data: Discovery, capture, curation, design, creation. In Proceedings of the 2019 CHI conference on human factors in computing systems. ACM, New York, USA, 1–15.
- Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases. In 2022 IEEE Visualization and Visual Analytics (VIS). IEEE, New York, NY, USA, 45–49. https://doi.org/10.1109/VIS54862.2022.00018
- Andrew Myers. 2022. In Human-Centered AI, the Boundaries Between UX and Software Roles Are Evolving. Stanford University. https://hai.stanford.edu/news/human-centered-ai-boundaries-between-ux-and-software-roles-are-evolving
- Pipelineprofiler: A visual analytics tool for the exploration of automl pipelines. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2020), 390–400.
- OpenAI. 2023a. GPT-3.5. https://platform.openai.com/docs/models/gpt-3-5
- OpenAI. 2023b. GPT best practices. https://platform.openai.com/docs/guides/gpt-best-practices
- Slide Gestalt: Automatic Structure Extraction in Slide Decks for Non-Visual Access. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 829, 14 pages.
- Jeffrey M Perkel. 2018. Why Jupyter is data scientists’ computational notebook of choice. Nature 563, 7732 (2018), 145–147.
- How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 131 (apr 2021), 25 pages. https://doi.org/10.1145/3449205
- Lodestar: Supporting Independent Learning and Rapid Experimentation Through Data-Driven Analysis Recommendations. arXiv:2204.07876
- Garr Reynolds. 2011. Presentation Zen: Simple ideas on presentation design and delivery. New Riders, California, USA.
- Aiding collaborative reuse of computational notebooks with annotated cell folding. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–12.
- Exploration and explanation in computational notebooks. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, USA, 1–12.
- Jeff Sauro and James R Lewis. 2016. Quantifying the user experience: Practical statistics for user research. Morgan Kaufmann, San Francisco, CA, USA.
- SlideGen: an abstractive section-based slide generator for scholarly documents. In Proceedings of the 21st ACM Symposium on Document Engineering (Limerick, Ireland) (DocEng ’21). Association for Computing Machinery, New York, NY, USA, Article 11, 4 pages. https://doi.org/10.1145/3469096.3474939
- Tractus: Understanding and supporting source code experimentation in hypothesis-driven data science. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–12.
- From individual to collaborative: the evolution of prism, a hybrid laboratory notebook. In Proceedings of the 2008 ACM conference on Computer supported cooperative work. Association for Computing Machinery, New York, NY, USA, 569–578.
- Documentation matters: Human-centered AI system to assist data science code documentation in computational notebooks. ACM Transactions on Computer-Human Interaction 29, 2 (2022), 1–33.
- How much automation does a data scientist want? arXiv preprint arXiv:2101.03970 abs/2101.03970 (2021).
- Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 364, 18 pages. https://doi.org/10.1145/3544548.3580753
- Datashot: Automatic generation of fact sheets from tabular data. IEEE transactions on visualization and computer graphics 26, 1 (2019), 895–905.
- Stickyland: Breaking the linear presentation of computational notebooks. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. Association for Computing Machinery, New York, NY, USA, 1–7.
- Emergent Abilities of Large Language Models. arXiv:2206.07682
- Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 (2022), 24824–24837.
- Fork It: Supporting Stateful Alternatives in Computational Notebooks. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 307, 12 pages. https://doi.org/10.1145/3411764.3445527
- Albireo: An Interactive Tool for Visually Summarizing Computational Notebook Structure. In 2019 IEEE Visualization in Data Science (VDS). IEEE, New York, NY, USA, 1–10. https://doi.org/10.1109/VDS48975.2019.8973385
- Presentation skills for scientists: a practical guide. Cambridge University Press, Cambridge, England.
- How do Data Science Workers Collaborate? Roles, Workflows, and Tools. Proc. ACM Hum.-Comput. Interact. 4, CSCW1, Article 22 (may 2020), 23 pages. https://doi.org/10.1145/3392826
- Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 53, 20 pages. https://doi.org/10.1145/3491102.3517615
- Ingrid Zukerman and Diane Litman. 2001. Natural language processing and user modeling: Synergies and limitations. User modeling and user-adapted interaction 11 (2001), 129–158.