Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool
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Abstract (arXiv)
We explore the design of Marvista -- a human-AI collaborative tool that employs a suite of natural language processing models to provide end-to-end support for reading online news articles. Before reading an article, Marvista helps a user plan what to read by filtering text based on how much time one can spend and what questions one is interested to find out from the article. During reading, Marvista helps the user reflect on their understanding of each paragraph with AI-generated questions. After reading, Marvista generates an explainable human-AI summary that combines both AI's processing of the text, the user's reading behavior, and user-generated data in the reading process. In contrast to prior work that offered (content-independent) interaction techniques or devices for reading, Marvista takes a human-AI collaborative approach that contributes text-specific guidance (content-aware) to support the entire reading process.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jul 2022.
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Citing papers per year: 2024: 2; 2025: 0; 2026: 2.
4 papers.
- 2024SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Modelcited by 3
- 2026SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answeringcited by 2
- 2024ArguMentor: Augmenting User Experiences with Counter-Perspectivescited by 0
- 2026How College Students Use AI to Navigate Course Readings: Evidence from an Eight-Week Studycited by 0
- 2026SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering14 Feb 2026
- 2026How College Students Use AI to Navigate Course Readings: Evidence from an Eight-Week Study10 Feb 2026
- 2024ArguMentor: Augmenting User Experiences with Counter-Perspectives4 Jun 2024
- 2024SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model21 Apr 2024