Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries
Yi Yu, Adam Jatowt, Antoine Doucet, Kazunari Sugiyama, Masatoshi Yoshikawa
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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 time-line for each story.To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.