Jin Yao Chin, Sourav S. Bhowmick, Adam Jatowt
<span title="">2017</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="" style="color: black;">Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR &#39;17</a> </i> &nbsp;
Tweets summarization aims to find a group of representative tweets for a specific 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 (i.e., 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 demonstration we present a
more &raquo; ... ghtweight, personalized, on-demand, topic modeling-based tweets summarization engine called totem, designed for such devices. Specifically, totem summarizes most recent tweets on a user's timeline and enables her to visualize and navigate representative topics and associated tweets in a user-friendly tap-and-swipe manner.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1145/3077136.3084138</a> <a target="_blank" rel="external noopener" href="">dblp:conf/sigir/ChinBJ17</a> <a target="_blank" rel="external noopener" href="">fatcat:fj5ud324c5dxrjixqwwvfbghw4</a> </span>
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