In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding timeline for each story. To achieve this, we propose a novel unsuperviscd summarization framework based on the two-stage affinity propagation process. We also introduce a quantitative evaluation measure for MTLS based on the previous TLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can provide better results than TLS.
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