Oujing Liu

dblp:344/8508 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-3038-2196ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
computational notebook
0.712023
Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration · CHI 2023
Visualization and visual analytics › visual communication
presentation generation
0.712023
Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration · CHI 2023
Human-AI interaction
human-AI collaboration
0.712023
Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration · CHI 2023

Methods — techniques the papers use, named apart from their topics

natural language processing · 1.3large language model · 1.3
YearPublicationVenuePosition
2023 Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration
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.
Fengjie Wang, Xuye Liu, Oujing Liu, Ali Neshati, Tengfei Ma 0001, Min Zhu 0005, Jian Zhao 0010
CHI3