Totem

Chin, Jin Yao; Bhowmick, Sourav S.; Jatowt, Adam

doi:10.1145/3077136.3084138

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Consequently, it is lightweight in design in order to tackle limited memory, limited processing power, limited network connectivity, and the small screen size of mobile devices. A user can simply visualize the summary by using a tap‐and‐swipe approach (Chin et al, ).…”

Section: Introductionmentioning

confidence: 99%

On‐demand recent personal tweets summarization on mobile devices

Chin

Bhowmick

Jatowt

2019

Asso for Info Science & Tech

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Tweets summarization aims to find a group of representative tweets for a specific set of input tweets or a given topic. In recent times, there have been several research efforts toward devising a variety of techniques to summarize tweets in Twitter. However, these techniques are either not personal (that is, consider only tweets in the timeline of a specific user) or are too expensive to be realized on a mobile device. Given that 80% of active Twitter users access the site on mobile devices, in this article we present a lightweight, personal, on‐demand, topic modeling‐based tweets summarization engine called TOTEM, designed for such devices. Specifically, TOTEM first preprocesses recent tweets in a user's timeline and exploits Latent Dirichlet Allocation‐based topic modeling to assign each preprocessed tweet to a topic. Then it generates a ranked list of relevant tweets, a topic label, and a topic summary for each of the topics. Our experimental study with real‐world data sets demonstrates the superiority of TOTEM.

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Section: Introductionmentioning

confidence: 99%