Abstract: In this paper we are focusing on the problem of short text summarization on the comment stream of a specific message from social network services (SNS). The quantity of comments may increase at a high rate after a social message is published, due to the high popularity of SNS.Then users may desire to get a brief understanding of a comment stream without reading the whole comment list, we attempt to group comments with similar content together and generate a concise opinion summary for this message. Since distinct users will request the summary at any moment, existing clustering methods cannot be directly applied and cannot meet the real-time need of this application. In this paper, we model a novel incremental clustering problem for comment stream summarization on SNS. Moreover, we propose IncreSTS algorithm that can incrementally update clustering results with latest incoming comments in real time. Furthermore, we design an at-a-glance visualization interface to help users easily and rapidly get an overview summary .IncreSTS possesses the advantages of high efficiency, high scalability, and better handling outliers.
Keywords—real-time short text summarization, incremental clustering, comment streams, social network services
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