19. A computer-implemented method for scoring documents, comprising: (a) receiving document text for a current document that includes at least two words; (b) calculating a prominence score and a descriptiveness score for each word from the current document and each pair of consecutive words from the current document, wherein: the prominence score for each word is based on a term weight and a non-compound likelihood for each word, the prominence score for each pair of consecutive words is based on a term weight and a bigram probability for each pair of consecutive words, a descriptiveness score for each word is based on a corpus, a descriptiveness score for each pair of consecutive words is based on the corpus; and (c) creating a scoring vector for the current document, wherein the scoring vector is based on the combination of each prominence score and each descriptiveness score.
19. A computer-implemented method for scoring documents, comprising: (a) receiving document text for a current document that includes at least two words; (b) calculating a prominence score and a descriptiveness score for each word from the current document and each pair of consecutive words from the current document, wherein: the prominence score for each word is based on a term weight and a non-compound likelihood for each word, the prominence score for each pair of consecutive words is based on a term weight and a bigram probability for each pair of consecutive words, a descriptiveness score for each word is based on a corpus, a descriptiveness score for each pair of consecutive words is based on the corpus; and (c) creating a scoring vector for the current document, wherein the scoring vector is based on the combination of each prominence score and each descriptiveness score. 21. The method of claim 19 , wherein the bigram probability comprises a number of times each respective word pair appears in a training corpus and a number of times a first word and a second word of each respective word pair appear within a certain distance of each other in the training corpus.