Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool

July 18, 2022 Β· Declared Dead Β· πŸ› ACM Trans. Comput. Hum. Interact.

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Authors Xiang 'Anthony' Chen, Chien-Sheng Wu, Lidiya Murakhovs'ka, Philippe Laban, Tong Niu, Wenhao Liu, Caiming Xiong arXiv ID 2207.08401 Category cs.HC: Human-Computer Interaction Citations 21 Venue ACM Trans. Comput. Hum. Interact. Last Checked 4 months ago
Abstract
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.
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