skip to main content
10.1145/3544548.3580753acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration

Published: 19 April 2023 Publication History

Abstract

Data scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks.

Supplementary Material

Supplemental Materials (3544548.3580753-supplemental-materials.zip)
MP4 File (3544548.3580753-video-figure.mp4)
Video Figure
MP4 File (3544548.3580753-video-preview.mp4)
Video Preview
MP4 File (3544548.3580753-talk-video.mp4)
Pre-recorded Video Presentation

References

[1]
Damian Avila. 2019. RISE. https://github.com/damianavila/RISE
[2]
Benjamin Bach, Zezhong Wang, Matteo Farinella, Dave Murray-Rust, and Nathalie Henry Riche. 2018. Design patterns for data comics. In Proceedings of the 2018 chi conference on human factors in computing systems. Association for Computing Machinery, New York, NY, USA, 1–12.
[3]
Ori Bar El, Tova Milo, and Amit Somech. 2020. Automatically generating data exploration sessions using deep reinforcement learning. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, New York, NY, USA, 1527–1537.
[4]
Jeff Barnes. 2015. Azure machine learning. In Microsoft Azure Essentials. Microsoft, Washington, DC, USA.
[5]
Matthias Blohm, Glorianna Jagfeld, Ekta Sood, Xiang Yu, and Ngoc Thang Vu. 2018. Comparing attention-based convolutional and recurrent neural networks: Success and limitations in machine reading comprehension. arXiv preprint arXiv:1808.08744 abs/1808.08744 (2018).
[6]
Matthew Brehmer and Robert Kosara. 2021. From jam session to recital: Synchronous communication and collaboration around data in organizations. arXiv preprint arXiv:2107.09042 abs/2107.09042 (2021).
[7]
Souti Chattopadhyay, Ishita Prasad, Austin Z Henley, Anita Sarma, and Titus Barik. 2020. 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. Association for Computing Machinery, New York, NY, USA, 1–12.
[8]
Colin Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, and Neel Sundaresan. 2020. PyMT5: multi-mode translation of natural language and Python code with transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 9052–9065.
[9]
David Donoho. 2017. 50 years of data science. Journal of Computational and Graphical Statistics 26, 4(2017), 745–766.
[10]
Ian Drosos, Titus Barik, Philip J Guo, Robert DeLine, and Sumit Gulwani. 2020. 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. Association for Computing Machinery, New York, NY, USA, 1–12.
[11]
Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes, and Burkhard Rost. 2021. CodeTrans: Towards Cracking the Language of Silicon’s Code Through Self-Supervised Deep Learning and High Performance Computing. arXiv preprint arXiv:2104.02443 abs/2104.02443 (2021).
[12]
Will Epperson, Doris Jung-Lin Lee, Leijie Wang, Kunal Agarwal, Aditya G Parameswaran, Dominik Moritz, and Adam Perer. 2022. Leveraging Analysis History for Improved In Situ Visualization Recommendation. In Computer Graphics Forum, Vol. 41. Wiley Online Library, USA, 145–155.
[13]
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, 2020. CodeBERT: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 abs/2002.08155 (2020).
[14]
Tsu-Jui Fu, William Yang Wang, Daniel McDuff, and Yale Song. 2022. Doc2PPT: Automatic presentation slides generation from scientific documents. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. Association for the Advancement of Artificial Intelligence, California, CA, USA, 634–642.
[15]
Sainyam Galhotra, Udayan Khurana, Oktie Hassanzadeh, Kavitha Srinivas, Horst Samulowitz, and Miao Qi. 2019. Automated Feature Enhancement for Predictive Modeling using External Knowledge. In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, New York, NY, USA, 1094–1097. https://doi.org/10.1109/ICDMW.2019.00161
[16]
Heng Gong, Xiaocheng Feng, Bing Qin, and Ting Liu. 2019. 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.
[17]
Google. 2021. Cloud AutoML Custom Machine Learning Models. https://cloud.google.com/automl
[18]
Philip J. Guo and Margo I. Seltzer. 2012. BURRITO: Wrapping Your Lab Notebook in Computational Infrastructure. In 4th Workshop on the Theory and Practice of Provenance, TaPP’12, Boston, MA, USA, June 14-15, 2012. USENIX Association, Boston, MA, USA.
[19]
Jiawei Han, Jian Pei, and Hanghang Tong. 2022. Data mining: concepts and techniques. Morgan kaufmann, San Francisco, CA.
[20]
Andrew Head, Fred Hohman, Titus Barik, Steven Mark Drucker, and Robert DeLine. 2019. Managing Messes in Computational Notebooks. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019. ACM, Glasgow, Scotland, UK, 270.
[21]
Andrew Head, Jason Jiang, James Smith, Marti A. Hearst, and Björn Hartmann. 2020. Composing Flexibly-Organized Step-by-Step Tutorials from Linked Source Code, Snippets, and Outputs. In CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020. ACM, New York, USA, 1–12.
[22]
Jeffrey Heer. 2019. Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences 116, 6 (2019), 1844–1850.
[23]
Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, and Kush R. Varshney. 2018. Increasing Trust in AI Services through Supplier’s Declarations of Conformity. CoRR abs/1808.07261(2018).
[24]
Weixiang Hong, Kaixiang Ji, Jiajia Liu, Jian Wang, Jingdong Chen, and Wei Chu. 2021. GilBERT: Generative Vision-Language Pre-Training for Image-Text Retrieval. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21). Association for Computing Machinery, New York, NY, USA, 1379–1388. https://doi.org/10.1145/3404835.3462838
[25]
Youyang Hou and Dakuo Wang. 2017. Hacking with NPOs: Collaborative Analytics and Broker Roles in Civic Data Hackathons. Proceedings of the ACM on Human-Computer Interaction 1 (12 2017), 1–16. https://doi.org/10.1145/3134688
[26]
Yue Hu and Xiaojun Wan. 2014. PPSGen: Learning-based presentation slides generation for academic papers. IEEE transactions on knowledge and data engineering 27, 4(2014), 1085–1097.
[27]
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. In Proceedings of the 38th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 139). PMLR, Breckenridge, Colorado, USA, 4904–4916.
[28]
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
[29]
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
[30]
Project Jupyter. 2021. nbconvert. Project Jupyter. https://github.com/jupyter/nbconvert
[31]
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020. Learning and Evaluating Contextual Embedding of Source Code. In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 119). PMLR, Breckenridge, Colorado, USA, 5110–5121.
[32]
DaYe Kang, Tony Ho, Nicolai Marquardt, Bilge Mutlu, and Andrea Bianchi. 2021. ToonNote: Improving communication in computational notebooks using interactive data comics. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, United States, 1–14.
[33]
Mary Beth Kery, Marissa Radensky, Mahima Arya, Bonnie E John, and Brad A Myers. 2018. 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.
[34]
Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel. 2016. The emerging role of data scientists on software development teams. In 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE). IEEE, ACM, New York, NY, USA, 96–107.
[35]
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian E Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica B Hamrick, Jason Grout, Sylvain Corlay, 2016. Jupyter Notebooks-a publishing format for reproducible computational workflows. Vol. 2016. IOS Press, Virginia, USA.
[36]
Laura Koesten, Emilia Kacprzak, Jeni Tennison, and Elena Simperl. 2019. Collaborative practices with structured data: Do tools support what users need?. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, New York, USA, 1–14.
[37]
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.
[38]
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.
[39]
Erin LeDell and Sebastien Poirier. 2020. H2O AutoML: Scalable Automatic Machine Learning. In Proceedings of the AutoML Workshop at ICML, Vol. 2020. automl.org, Freiburg and Hannover, Germany.
[40]
Xingjun Li, Yuanxin Wang, Hong Wang, Yang Wang, and Jian Zhao. 2021. 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.
[41]
Xingjun Li, Yizhi Zhang, Justin Leung, Chengnian Sun, and Jian Zhao. 2021. EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation. https://doi.org/10.48550/ARXIV.2112.07858
[42]
Denghui Liu, Chi Xu, Wenjun He, Zhimeng Xu, Wenqi Fu, Lei Zhang, Jie Yang, Zhihao Wang, Bing Liu, Guangdun Peng, 2021. AutoGenome: an autoML tool for genomic research. Artificial Intelligence in the Life Sciences 1 (2021), 100017.
[43]
Ke Liu, Guang Yang, Xiang Chen, and Chi Yu. 2022. SOTitle: A Transformer-based Post Title Generation Approach for Stack Overflow. CoRR abs/2202.09789(2022).
[44]
Xuye Liu, Dakuo Wang, April Yi Wang, Yufang Hou, and Lingfei Wu. 2021. HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks. In Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021. Association for Computational Linguistics, Pennsylvania, USA, 4473–4485.
[45]
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota. 2021. Studying the usage of text-to-text transfer transformer to support code-related tasks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, IEEE, New York, NY, USA, 336–347.
[46]
Andreas Mathisen, Tom Horak, Clemens Nylandsted Klokmose, Kaj Grønbæk, and Niklas Elmqvist. 2019. InsideInsights: Integrating Data-Driven Reporting in Collaborative Visual Analytics. Comput. Graph. Forum 38, 3 (2019), 649–661.
[47]
Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q Vera Liao, Casey Dugan, and Thomas Erickson. 2019. 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.
[48]
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
[49]
Microsoft Office. 2021. Make Your PowerPoint Presentations Accessible to People with Disabilities. https://support.microsoft.com/en-us/office/make-your-powerpoint-presentations-accessible-to-people-with-disabilities-6f7772b2-2f33-4bd2-8ca7-dae3b2b3ef25
[50]
Samir Passi and Steven J Jackson. 2018. Trust in data science: Collaboration, translation, and accountability in corporate data science projects. Proceedings of the ACM on Human-Computer Interaction 2, CSCW(2018), 1–28.
[51]
Jeffrey M Perkel. 2018. Why Jupyter is data scientists’ computational notebook of choice. Nature 563, 7732 (2018), 145–147.
[52]
David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, and Felix Portnoy. 2021. How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1(2021), 1–25.
[53]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. https://doi.org/10.48550/ARXIV.1910.10683
[54]
Bernadette M Randles, Irene V Pasquetto, Milena S Golshan, and Christine L Borgman. 2017. Using the Jupyter notebook as a tool for open science: An empirical study. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL). IEEE, IEEE Computer Society, Washington, DC, USA, 1–2.
[55]
Adam Rule, Ian Drosos, Aurélien Tabard, and James D Hollan. 2018. Aiding collaborative reuse of computational notebooks with annotated cell folding. Proceedings of the ACM on Human-Computer Interaction 2, CSCW(2018), 1–12.
[56]
Adam Rule, Aurélien Tabard, and James D Hollan. 2018. Exploration and explanation in computational notebooks. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, New York, USA, 1–12.
[57]
Cliff Click SriSatish Ambati. 2021. H2O. H2O.ai. https://h2o.ai
[58]
Krishna Subramanian, Johannes Maas, and Jan Borchers. 2020. 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. ACM, New York, USA, 1–12.
[59]
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, and Nancy Xin Ru Wang. 2021. D2S: Document-to-Slide Generation Via Query-Based Text Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021. Association for Computational Linguistics, Pennsylvania, USA, 1405–1418.
[60]
Harini Suresh, Steven R Gomez, Kevin K Nam, and Arvind Satyanarayan. 2021. Beyond expertise and roles: A framework to characterize the stakeholders of interpretable machine learning and their needs. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, New York, USA, 1–16.
[61]
Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis, and Omiros Ragos. 2019. Implementing AutoML in educational data mining for prediction tasks. Applied Sciences 10, 1 (2019), 90.
[62]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[63]
April Yi Wang, Dakuo Wang, Jaimie Drozdal, Michael Muller, Soya Park, Justin D Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan. 2022. 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.
[64]
April Yi Wang, Zihan Wu, Christopher Brooks, and Steve Oney. 2020. Callisto: Capturing the" Why" by Connecting Conversations with Computational Narratives. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, New York, USA, 1–13.
[65]
Dakuo Wang, Q Vera Liao, Yunfeng Zhang, Udayan Khurana, Horst Samulowitz, Soya Park, Michael Muller, and Lisa Amini. 2021. How much automation does a data scientist want?arXiv preprint arXiv:2101.03970 abs/2101.03970 (2021).
[66]
Dakuo Wang, Lingfei Wu, Xuye Liu, Yi Wang, Chuang Gan, Jing Xu, Xue Ying Zhang, and Jun Wang. 2022. Learning-based automated machine learning code annotation with graph neural network. US Patent App. 17/088,018.
[67]
Dakuo Wang, Lingfei Wu, Yi Wang, Xuye Liu, Chuang Gan, Si Er Han, Bei Chen, and Ji Hui Yang. 2022. Learning-based automation machine learning code annotation in computational notebooks. US Patent App. 17/069,402.
[68]
Yun Wang, Zhida Sun, Haidong Zhang, Weiwei Cui, Ke Xu, Xiaojuan Ma, and Dongmei Zhang. 2019. DataShot: Automatic generation of fact sheets from tabular data. IEEE transactions on visualization and computer graphics 26, 1(2019), 895–905.
[69]
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021. CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859 abs/2109.00859 (2021).
[70]
Zezhong Wang, Shunming Wang, Matteo Farinella, Dave Murray-Rust, Nathalie Henry Riche, and Benjamin Bach. 2019. Comparing effectiveness and engagement of data comics and infographics. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, New York, USA, 1–12.
[71]
Zijie J Wang, Katie Dai, and W Keith Edwards. 2022. StickyLand: Breaking the Linear Presentation of Computational Notebooks. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. ACM, New York, USA, 1–7.
[72]
John Wenskovitch, Jian Zhao, Scott Carter, Matthew Cooper, and Chris North. 2019. Albireo: An interactive tool for visually summarizing computational notebook structure. In 2019 IEEE visualization in data science (VDS). IEEE, IEEE, New York, NY, USA, 1–10.
[73]
Xueqing Wu, Jiacheng Zhang, and Hang Li. 2022. Text-to-Table: A New Way of Information Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022. Association for Computational Linguistics, Pennsylvania, USA, 2518–2533.
[74]
Yifan Wu, Joseph M Hellerstein, and Arvind Satyanarayan. 2020. B2: Bridging code and interactive visualization in computational notebooks. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology. ACM, New York, USA. https://doi.org/10.1145/3379337.3415851
[75]
Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee, Niloufar Salehi, and Aditya Parameswaran. 2021. Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows, In CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021. CoRR abs/2101.04834, 83:1–83:16.
[76]
Amy X Zhang, Michael Muller, and Dakuo Wang. 2020. How do data science workers collaborate? roles, workflows, and tools. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1(2020), 1–23.
[77]
Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, DongGyun Han, David Lo, and Lingxiao Jiang. 2022. iTiger: An automatic issue title generation tool. arXiv preprint arXiv:2206.10811 abs/2206.10811 (2022), 1637–1641.
[78]
Jian Zhao, Shenyu Xu, Senthil Chandrasegaran, Chris Bryan, Fan Du, Aditi Mishra, Xin Qian, Yiran Li, and Kwan-Liu Ma. 2021. ChartStory: Automated partitioning, layout, and captioning of charts into comic-style narratives. arXiv preprint arXiv:2103.03996 29, 2 (2021), 1384–1399.
[79]
Chengbo Zheng, Dakuo Wang, April Yi Wang, and Xiaojuan Ma. 2022. Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work. In CHI Conference on Human Factors in Computing Systems. ACM, New York, USA, 1–20.
[80]
Ingrid Zukerman and Diane Litman. 2001. Natural language processing and user modeling: Synergies and limitations. User modeling and user-adapted interaction 11, 1 (2001), 129–158.

Cited By

View all
  • (2024)Future Trends in AI and Academic Research WritingUtilizing AI Tools in Academic Research Writing10.4018/979-8-3693-1798-3.ch015(232-254)Online publication date: 12-Apr-2024
  • (2024)Development of Animated Drawings Based Presentation Authoring Tool for Creative ExpressionThe Journal of Korean Association of Computer Education10.32431/kace.2024.27.3.01227:3(135-144)Online publication date: 31-May-2024
  • (2024)anywidget: reusable widgets for interactive analysis and visualization in computational notebooksJournal of Open Source Software10.21105/joss.069399:102(6939)Online publication date: Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
April 2023
14911 pages
ISBN:9781450394215
DOI:10.1145/3544548
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 April 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. computational notebooks
  2. data science.
  3. human-AI collaboration
  4. natural language processing
  5. slides generation

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

Conference

CHI '23
Sponsor:

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025
ACM CHI Conference on Human Factors in Computing Systems
April 26 - May 1, 2025
Yokohama , Japan

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)905
  • Downloads (Last 6 weeks)105
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Future Trends in AI and Academic Research WritingUtilizing AI Tools in Academic Research Writing10.4018/979-8-3693-1798-3.ch015(232-254)Online publication date: 12-Apr-2024
  • (2024)Development of Animated Drawings Based Presentation Authoring Tool for Creative ExpressionThe Journal of Korean Association of Computer Education10.32431/kace.2024.27.3.01227:3(135-144)Online publication date: 31-May-2024
  • (2024)anywidget: reusable widgets for interactive analysis and visualization in computational notebooksJournal of Open Source Software10.21105/joss.069399:102(6939)Online publication date: Oct-2024
  • (2024)NotePlayer: Engaging Computational Notebooks for Dynamic Presentation of Analytical ProcessesProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676410(1-20)Online publication date: 13-Oct-2024
  • (2024)MS Slide Designer: A Study on Human-AI Collaboration for Content CreationProceedings of the 16th Conference on Creativity & Cognition10.1145/3635636.3664259(499-503)Online publication date: 23-Jun-2024
  • (2024)SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational NotebooksExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650848(1-17)Online publication date: 11-May-2024
  • (2024)OutlineSpark: Igniting AI-powered Presentation Slides Creation from Computational Notebooks through OutlinesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642865(1-16)Online publication date: 11-May-2024
  • (2024)Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI CollaborationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642726(1-19)Online publication date: 11-May-2024
  • (2024)How Do Analysts Understand and Verify AI-Assisted Data Analyses?Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642497(1-22)Online publication date: 11-May-2024
  • (2024)MaugVLink: Augmenting Mathematical Formulas with Visual Links2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00048(337-342)Online publication date: 23-Apr-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media