Automatic multi-document summarization (MDS) is the process of extracting the most important information, such as events and entities, from multiple natural language texts focused on the same topic. In this paper, we experiment with the effects of different groups of information such as events and named entities in the domain of generic and update MDS. Our generic MDS system has outperformed the best recent generic MDS systems in DUC 2004 in terms of ROUGE-1 recall and f_1-measure. Update summarization is a new form of MDS, where novel yet salient sentences are chosen as summary sentences based on the assumption that the user has already read a given set of documents. We present an event based update summarization where the novelty is detected based on the temporal ordering of events, and the saliency is ensured by the event and entity distribution. To our knowledge, no other study has deeply experimented with the effects of the novelty information acquired from the temporal ordering of events (assuming that a sentence contains one or more events) in the domain of update multi-document summarization. Our update MDS system has outperformed the state-of-the-art update MDS system in terms of ROUGE-2 and ROUGE-SU4 recall measures. All our MDS systems also generate quality summaries which are manually evaluated based on popular evaluation criteria.
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