One commonly used method in both knowledge extraction and certain types of “rote” summarization relies on the use of extraction patterns; these are designed to match the kinds of conventional linguistic patterns typically used by authors to express the information of interest.  For example, text corpora or newswire might be mined for information about companies, by keying in on known company names and terms such as “Corp.”,  “.com”, “headquartered at”, and “annual revenue of”, as well as parts of speech and dependency relations, and matching regular-expression patterns against local text segments containing key phrases or positioned close to them. As another example, summarization of earthquake reports might extract expected information such as the epicenter of the quake, its magnitude on the Richter scale, the time and duration of the event, affected population centers, extent of death tolls, injuries, and property damage, consequences such as fires and tsunamis, etc. Extraction patterns can usually be thought of as targeting particular attributes in predetermined attribute-value frames (e.g., a frame for company information or a frame for facts about an earthquake), and the filled-in frames may themselves be regarded as summaries, or may be used to generate natural-language summaries. Early systems of this type were FRUMP (DeJong 1982) and JASPER (Andersen et al. 1992). Among the hundreds of more modern extraction systems, a particularly successful one in competitions has been SRI's “Fastus” (Hobbs et al. 1997).