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- W2089787183 abstract "This paper aims to generate digests of tweets from live trending and ongoing topics. The primary purpose is to group the tweets by importance or usefulness so that an end user can be presented with a reasonable extract of the most important content from the Twitter stream. Summarization is accomplished using a non-parametric Bayesian model applied to Hidden Markov Models and a novel observation model designed to allow ranking based on selected predictive characteristics of individual tweets." @default.
- W2089787183 created "2016-06-24" @default.
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- W2089787183 date "2014-12-01" @default.
- W2089787183 modified "2023-10-05" @default.
- W2089787183 title "Automatic Twitter Topic Summarization" @default.
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- W2089787183 doi "https://doi.org/10.1109/cse.2014.69" @default.
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