Finding and linking incidents in news

News reports are being produced and disseminated in overwhelming volume, making it difficult to keep up with the newest information. Most previous research in automatic news organization treated news topics as a flat list, ignoring the intrinsic connection among individual reports. We argue that more contextual information within and across the topics will benefit users in their news understanding process. A news organization infrastructure, incident threading, is proposed in this article. All text snippets describing the occurrence of a real-world happening are combined into a news incident, and a network is composed of incidents that are interconnected by links in certain types. A limited vocabulary of connection types is defined and corresponding rules are established based upon the human experience of news understanding. The incident threading system is implemented with two different algorithms. One starts from clustering of text passages and then creates links with pre-built rules. The other method defines a global score function over the whole collection and solves the optimization problem with simulated annealing. The former achieves higher accuracy in the identification of incidents and the latter generates better links, which is preferred since the links are more important for the formation of the incident network.