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Content Provider | ACM Digital Library |
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Author | Mehta, Sameep Malla, Ravee Goyal, Rahul Ramanath, Maya Bagchi, Amitabha |
Abstract | Providing the history and context(s) of a news article that emerges in the middle of an evolving news story--sometimes multiple news stories--is a complex task. The complexity of the task is compounded by the fact that different users are interested in different contexts of the article, and it is impossible to guess what a particular user is most interested in. In this paper, we introduce ESTHETE, a system that provides rich context(s) (through what we call personalized flexible context extraction), by preprocessing and storing articles in a structured representation (directed graphs) that makes it easy for the user to explore different contexts. The advantage of this approach is that the incremental computational expense in incorporating new articles as they are published is minimal. Our system is available at: http://konfrap.com/esthete. |
Starting Page | 2529 |
Ending Page | 2532 |
Page Count | 4 |
File Format | |
ISBN | 9781450322638 |
DOI | 10.1145/2505515.2508208 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2013-10-27 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | News browsing News story visualization Entity topic exploration |
Content Type | Text |
Resource Type | Article |
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