AG 5   Info

AG Audience

English

60 Minutes

Saarbrücken

Time-aware multi-viewpoint summarization monitors viewpoints for a running topic by selecting a set of informative documents. In this paper, we focus on time-aware multi-viewpoint summarization of multilingual social text streams. Viewpoint drift, sentiment polarization, ambiguous entities and multilingual text make our task a challenging problem. Our approach includes three core ingredients: dynamic viewpoint modeling, cross-language viewpoint alignment and multi-viewpoint summarization. Specifically, we propose a dynamic latent factor model to explicitly characterize a set of viewpoints through which entities, topics and sentiment labels during a time interval are derived jointly; we connect viewpoints in different languages by using an entity-based semantic similarity measure; we employ an update viewpoint summarization strategy to generate a time-aware summary to reflect viewpoints. Experiments conducted on a real-world dataset demonstrate the effectiveness of our proposed method in time-aware multi-viewpoint summarization of multilingual social text streams.

Daniela Alessi

5000

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