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Free-form text summarization in social sensing

Published: 16 April 2012 Publication History

Abstract

This demonstration illustrates an information aggregation and summarization service for social sensing applications. Social sensing, using mobile phones and other networked devices in the possession of individuals, has gained significant popularity in recent years. In some cases, the information collected is structured, such as numeric data from temperature sensors, accelerometers, or GPS devices. Aggregate statistical properties, such as expected values, standard deviations, and outliers, can be easily computed, and can be used to summarize the data set. In other cases, however, the collection includes unstructured data types such as text or images with textual annotations. The concepts of expected values and outliers are harder to define, yet it is still important to be able to aggregate and summarize the data. We demonstrate a system which can automatically summarize real-time textual data common to social sensing applications. Specifically, we focus on text messages that describe events in the environment. The output of our service provides a reliable summary of observations that can be used in many contexts from military intelligence to participatory sensing campaigns.

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Cited By

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  • (2015)Embracing Information Explosion without Choking: Clustering and Labeling in MicrobloggingIEEE Transactions on Big Data10.1109/TBDATA.2015.24516351:1(35-46)Online publication date: 1-Mar-2015

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    cover image ACM Conferences
    IPSN '12: Proceedings of the 11th international conference on Information Processing in Sensor Networks
    April 2012
    354 pages
    ISBN:9781450312271
    DOI:10.1145/2185677

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 April 2012

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    Author Tags

    1. free-form text
    2. information summarization
    3. social sensing

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    Overall Acceptance Rate 143 of 593 submissions, 24%

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    • (2015)Embracing Information Explosion without Choking: Clustering and Labeling in MicrobloggingIEEE Transactions on Big Data10.1109/TBDATA.2015.24516351:1(35-46)Online publication date: 1-Mar-2015

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