ScentIndex and ScentHighlights: Productive Reading Techniques for Conceptually Reorganizing Subject Indexes and Highlighting Passages

Author:

Chi Ed H.1,Hong Lichan1,Heiser Julie12,Card Stuart K.1,Gumbrecht Michelle12

Affiliation:

1. Palo Alto Research Center, User Interface Research, 3333 Coyote Hill Road, Palo Alto, CA 94304, U.S.A.

2. Work done while at PARC

Abstract

A great deal of analytical work has been carried out in the context of reading, in digesting the semantics of the material, the identification of important entities, and capturing the relationship between entities. Visual analytic environments, therefore, must encompass reading tools that enable the rapid digestion of large amounts of reading material. Other than plain text search, subject indexes, and basic highlighting, tools are needed for rapid foraging of the text. In this paper, we describe a technique that presents an enhanced subject index for a book by conceptually reorganizing it to suit particular expressed user information needs. Users first enter information needs via keywords, describing the concepts they are trying to retrieve and comprehend. Then our system, called ScentIndex, computes what index entries are conceptually related, and reorganizes and displays these index entries on a single page. We provide a number of navigational cues to help users peruse over this list of index entries and find relevant passages quickly. We report some initial results in a new technique called ScentHighlights that enhances skimming activity by conceptually highlighting sentences. Both use similar techniques by computing what conceptual keywords are related to each other via word co-occurrence and spreading activation. Compared to regular reading of a paper book, our study showed that users are more efficient and more accurate in finding, comparing, and comprehending material in our system.

Publisher

SAGE Publications

Subject

Computer Vision and Pattern Recognition

Reference31 articles.

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Toward Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual Analytics;ACM Transactions on Interactive Intelligent Systems;2024-01-09

2. With respect to what?;Proceedings of the 25th International Conference on Intelligent User Interfaces;2020-03-04

3. Machine Learning from User Interaction for Visualization and Analytics: A Workshop-Generated Research Agenda;2019 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI);2019-10-20

4. Same translation but different experience;Proceedings of the SIGCHI Conference on Human Factors in Computing Systems;2013-04-27

5. Domain Search and Exploration with Meta-Indexes;Research and Advanced Technology for Digital Libraries;2013

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