Summarizing Disasters Over Time
نویسندگان
چکیده
We have developed a text summarization system that can generate summaries over time from web crawls on disasters. We show that our method of identifying exemplar sentences for a summary using affinity propagation clustering produces better summaries than clustering based on K-medoids as measured using Rouge on a small set of examples. A key component of our approach is the prediction of salient information using event related features based on location, temporal changes in topic, and two different language models.
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