When a document to be summarized cannot be assumed to fall into some predictable category, with the content structured and expressed in a stereotyped way, summarization is usually performed by selecting and combining “central sentences” from the document. A sentence is central to the extent that many other sentences in the document are similar to it, in terms of shared word content or some more sophisticated similarity measure such as one based on the tf-idf metric for terms, or a cosine metric in a dimensionality-reduced vector space (thus it is as if we were treating individual sentences as documents, and finding a few sentences whose “relevance” to the remaining sentences is maximal). However, simply returning a sequence of central sentences will not in general yield an adequate summary. For example, such sentences may contain unresolved pronouns or other referring expressions, whose referents may need to be sought in non-central sentences. Also, central “sentences” may actually be clauses embedded in lengthier sentences that contain unimportant supplementary information. Heuristic techniques need to be applied to identify and excise the extra material, and extracted clauses need to be fluently and coherently combined. In other cases, complex descriptions should be more simply and abstractly paraphrased. For example, an appropriate condensation of a sentence such as “The tornado carried off the roof of a local farmhouse, and reduced its walls and contents to rubble” might be “The tornado destroyed a local farmhouse.” But while some of these issues are partially addressed in current systems, human-like summarization will require much deeper understanding than is currently attainable. Another difficulty in this area (even more so than in machine translation) is the evaluation of summaries. Even human judgments differ greatly, depending, for instance, on the sensitivity of the evaluator to grammatical flaws, versus inadequacies in content.