On-demand recent personal tweets summarization on mobile devices
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...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/144489 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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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