Cited By
View all- Felicini NMortara L(2023)Augmented Cognition Compass: A Taxonomy of Cognitive AugmentationsAugmented Cognition10.1007/978-3-031-35017-7_13(189-205)Online publication date: 9-Jul-2023
Text Summary Augmentation for Intelligent Reading Assistant
Pages 319 - 321
Abstract
This paper presents a technique to assist a reader. We aim to reduce manual efforts of the reader by leveraging the state-of-the-art document summarization techniques and providing summaries about unclear descriptions for each reader. Our system acts as a plug-and-play model that can be modified to support additional methodologies. As a backend of the system, we investigated several text summarization techniques and evaluated three techniques of them: TextRank, LexRank, and Luhn’s algorithm.
References
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Günes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research 22 (2004), 457–479.
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Shoya Ishimaru, Ko Watanabe, Nicolas Großmann, Carina Heisel, Pascal Klein, Yutaka Arakawa, Jochen Kuhn, and Andreas Dengel. 2018. Demonstration of HyperMind Builder: Pervasive User Interface to Create Intelligent Interactive Documents.
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Published In
February 2021
321 pages
ISBN:9781450384285
DOI:10.1145/3458709
Copyright © 2021 Owner/Author.
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
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Published: 11 July 2021
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View all- Felicini NMortara L(2023)Augmented Cognition Compass: A Taxonomy of Cognitive AugmentationsAugmented Cognition10.1007/978-3-031-35017-7_13(189-205)Online publication date: 9-Jul-2023
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