Telling Stories from Computational Notebooks: AI-Assisted Presentation
Slides Creation for Presenting Data Science Work
- URL: http://arxiv.org/abs/2203.11085v1
- Date: Mon, 21 Mar 2022 16:06:07 GMT
- Title: Telling Stories from Computational Notebooks: AI-Assisted Presentation
Slides Creation for Presenting Data Science Work
- Authors: Chengbo Zheng, Dakuo Wang, April Yi Wang, Xiaojuan Ma
- Abstract summary: This paper presents NB2Slides, an AI system that facilitates users to compose presentations of their data science work.
NB2Slides uses deep learning methods as well as example-based prompts to generate slides from computational notebooks.
It also provides an interactive visualization that links the slides with the notebook to help users further edit the slides.
- Score: 47.558611855454195
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Creating presentation slides is a critical but time-consuming task for data
scientists. While researchers have proposed many AI techniques to lift data
scientists' burden on data preparation and model selection, few have targeted
the presentation creation task. Based on the needs identified from a formative
study, this paper presents NB2Slides, an AI system that facilitates users to
compose presentations of their data science work. NB2Slides uses deep learning
methods as well as example-based prompts to generate slides from computational
notebooks, and take users' input (e.g., audience background) to structure the
slides. NB2Slides also provides an interactive visualization that links the
slides with the notebook to help users further edit the slides. A follow-up
user evaluation with 12 data scientists shows that participants believed
NB2Slides can improve efficiency and reduces the complexity of creating slides.
Yet, participants questioned the future of full automation and suggested a
human-AI collaboration paradigm.
Related papers
- PARK: Personalized academic retrieval with knowledge-graphs [6.879116518049676]
Academic search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers.<n>We propose a two-step approach: first, training a neural language model for retrieval, then converting the academic graph into a knowledge graph.<n>This allows user models to capture both explicit relationships and hidden structures in citation graphs and paper content.
arXiv Detail & Related papers (2025-07-18T13:41:01Z) - AI-Generated Lecture Slides for Improving Slide Element Detection and Retrieval [25.517836483457803]
We propose a large language model (LLM)-guided synthetic lecture slide generation pipeline, SynLecSlideGen.<n>We also create an evaluation benchmark, namely RealSlide by manually annotating 1,050 real lecture slides.<n> Experimental results show that few-shot transfer learning with pretraining on synthetic slides significantly improves performance compared to training only on real data.
arXiv Detail & Related papers (2025-06-30T08:11:31Z) - Talk to Your Slides: Language-Driven Agents for Efficient Slide Editing [28.792459459465515]
We propose Talk-to-Your-Slides, an agent to edit slides %in active PowerPoint sessions.<n>Our system enables 34.02% faster processing, 34.76% better instruction fidelity, and 87.42% cheaper operation than baselines.
arXiv Detail & Related papers (2025-05-16T18:12:26Z) - PASS: Presentation Automation for Slide Generation and Speech [0.0]
PASS is a pipeline used to generate slides from general Word documents.
It also automates the oral delivery of the generated slides.
Pass analyzes user documents to create a dynamic, engaging presentation with an AI-generated voice.
arXiv Detail & Related papers (2025-01-11T10:22:04Z) - DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generation [18.05133277269579]
We present a method to generate synthetic, structured visuals with target labels using code generation.
Our method allows people to create datasets with built-in labels and train models with a small number of human-annotated examples.
arXiv Detail & Related papers (2024-09-30T19:55:54Z) - Data Formulator 2: Iteratively Creating Rich Visualizations with AI [65.48447317310442]
We present Data Formulator 2, an LLM-powered visualization system to address these challenges.
With Data Formulator 2, users describe their visualization intent with blended UI and natural language inputs, and data transformation are delegated to AI.
To support iteration, Data Formulator 2 lets users navigate their iteration history and reuse previous designs towards new ones so that they don't need to start from scratch every time.
arXiv Detail & Related papers (2024-08-28T20:12:17Z) - Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models [0.0]
Few-Shot Prompting or in-context learning enables models to recognize entities with minimal examples.
We assess state-of-the-art models like GPT-4 in NER tasks, comparing their few-shot performance to fully supervised benchmarks.
arXiv Detail & Related papers (2024-08-28T13:42:28Z) - AUGUST: an Automatic Generation Understudy for Synthesizing
Conversational Recommendation Datasets [56.052803235932686]
We propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues.
In doing so, we exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets.
arXiv Detail & Related papers (2023-06-16T05:27:14Z) - Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation
with Large Language Models [116.25562358482962]
State-of-the-art neural language models can be used to solve ad-hoc language tasks without the need for supervised training.
PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts.
arXiv Detail & Related papers (2022-08-16T17:17:53Z) - D2S: Document-to-Slide Generation Via Query-Based Text Summarization [27.576875048631265]
We contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years' NLP and ML conferences.
Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach.
Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.
arXiv Detail & Related papers (2021-05-08T10:29:41Z) - DOC2PPT: Automatic Presentation Slides Generation from Scientific
Documents [76.19748112897177]
We present a novel task and approach for document-to-slide generation.
We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner.
Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides.
arXiv Detail & Related papers (2021-01-28T03:21:17Z) - Learning to Emphasize: Dataset and Shared Task Models for Selecting
Emphasis in Presentation Slides [31.540208729354354]
Emphasizing strong leading words in presentation slides can allow the audience to direct the eye to certain focal points instead of reading the entire slide.
Motivated by this demand, we study the problem of Emphasis Selection (ES) in presentation slides.
We introduce a new dataset containing presentation slides with a wide variety of topics, each is annotated with emphasis words in a crowdsourced setting.
arXiv Detail & Related papers (2021-01-02T06:54:55Z) - Mining Implicit Entity Preference from User-Item Interaction Data for
Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task.
Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.
To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
arXiv Detail & Related papers (2020-03-28T05:47:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.