1. A computer-implemented method implemented using instructions stored on a computer-readable medium and executable by a computing device, the method comprising: initializing event parameters to identify a number of salient events from a corpus of documents, wherein the events comprise occurrences that are described in the corpus of documents and are identified based on a salient score calculated from the distance between peaks on a graph, the peaks on the graph corresponding to each respective one of the events; probabilistically determining, using a generative model, whether documents are associated with a first event to detect representative events of the number of salient events, wherein probabilistically determining comprises: estimating parameters for the generative model using the event parameters; generating event clusters to cluster events reported by the documents using estimated generative model parameters; for each event cluster: increasing or decreasing a number of events to represent a corresponding event; if the number of events is not a minimum or a maximum number of events: (a) again performing operations associated with initializing the event parameters to generate re-initialized event parameters; and (b) using the generative model to probabilistically detect events from salient ones of the documents using the re-initialized event parameters; and if the number of events is a minimum or a maximum number of events, summarizing event(s) associated with the event cluster to assign content of one or more documents to respective events; selecting the first event reported by one or more of the documents; and for each entity associated with the first event: generating a respective news article for the first event; and determining a time for the respective news article.
1. A computer-implemented method implemented using instructions stored on a computer-readable medium and executable by a computing device, the method comprising: initializing event parameters to identify a number of salient events from a corpus of documents, wherein the events comprise occurrences that are described in the corpus of documents and are identified based on a salient score calculated from the distance between peaks on a graph, the peaks on the graph corresponding to each respective one of the events; probabilistically determining, using a generative model, whether documents are associated with a first event to detect representative events of the number of salient events, wherein probabilistically determining comprises: estimating parameters for the generative model using the event parameters; generating event clusters to cluster events reported by the documents using estimated generative model parameters; for each event cluster: increasing or decreasing a number of events to represent a corresponding event; if the number of events is not a minimum or a maximum number of events: (a) again performing operations associated with initializing the event parameters to generate re-initialized event parameters; and (b) using the generative model to probabilistically detect events from salient ones of the documents using the re-initialized event parameters; and if the number of events is a minimum or a maximum number of events, summarizing event(s) associated with the event cluster to assign content of one or more documents to respective events; selecting the first event reported by one or more of the documents; and for each entity associated with the first event: generating a respective news article for the first event; and determining a time for the respective news article. 5. The method of claim 1 , wherein document content associated with an event of the respective events comprises one or more of information associated with persons, locations, and keywords and wherein probabilistically determining further comprises: modeling the document content with mixtures of different respective unigram models; and modeling the timestamps with a mixture model that is not one of the different respective unigram models.