p272-chi - ScentHighlights Highlighting...

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ScentHighlights: Highlighting Conceptually-Related Sentences during Reading Ed H. Chi, Lichan Hong, Michelle Gumbrecht, Stuart K. Card Palo Alto Research Center 3333 Coyote Hill Road, Palo Alto, CA 94304 {echi, hong, mgumbrec, card}@parc.com ABSTRACT Researchers have noticed that readers are increasingly skimming instead of reading in depth. Skimming also occur in re-reading activities, where the goal is to recall specific topical facts. Bookmarks and highlighters were invented precisely to achieve this goal. For skimming activities, readers need effective ways to direct their attention toward the most relevant passages within text. We describe how we have enhanced skimming activity by conceptually highlighting sentences within electronic text that relate to search keywords. We perform the conceptual highlighting by computing what conceptual keywords are related to each other via word co-occurrence and spreading activation. Spreading activation is a cognitive model developed in psychology to simulate how memory chunks and conceptual items are retrieved in our brain. We describe the method used, and illustrate the idea with realistic scenarios using our system. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces— Graphical User Interfaces ; H.5.4 [Information Interfaces and Presentation]: Hypertext/Hypermedia—Navigation; User Issues H.5.m. [Information interfaces and presentation (e.g., HCI)]: Miscellaneous General Terms: Design, Human Factors. Keywords dynamic summarization, automatic text highlighting, contextualization, personalized information access, eBooks, Information Scent. INTRODUCTION Reading is a unique and essential human activity that furthers the mind and soul of our collective knowledge and history [6]. Therefore, Human-Information Interaction must directly study and make sense of the reading activity [11]. Reading as an activity is governed by the complexity of the information environment in which it occurs. Increasingly, reading is occurring online in blogs and on the web, and less so on paper. Moreover, readers tend to skim quickly for relevant information nuggets instead of analyzing a piece of text for deep meaning. This fundamental shift in reading is what motivated many researchers to examine the possibilities for enhancing modern-day reading activities [8, 7]. For the purpose of skimming, we are exploring ways to automatically highlight potentially relevant sentences and passages in electronic text using conceptual modeling. Figure 1 below shows an example of the sentence and keyword highlights made by our system with the user profile interest of “Marburg” virus.
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