Patent ID: 7562066

Claim:
A computer-implemented method for extracting important document segments from an input document, comprising: detecting k terms that occur in said input document; segmenting said input document into N document segments, each segment being a predetermined part of said document; generating document segment vectors, each vector including as its element values occurrence of frequencies of said terms occurring in said document segments, wherein a n-th document vector d n (n=1, . . . , N) is represented by (d n1 , d n2 , . . . , d nk ) and d ni represents the occurrence frequency of an i-th term among a total k terms in a n-th document segment; determining eigenvalues and eigenvectors of a square sum matrix A, where said square sum matrix A is a k×k matrix, k>1, and each component A ab of said square sum matrix A indicates a degree of co-occurrence of a-th and b-th terms (a,b=1, . . . , k) in said input document and is calculated by: A a ⁢ ⁢ b = ∑ n = 1 N ⁢ d n ⁢ ⁢ a ⁢ d n ⁢ ⁢ b , and a rank of said square sum matrix is represented by R; selecting, from said eigenvectors, a plural of (L) eigenvectors to be used for determining importance; calculating a weighted sum of squared projections of said document segment vectors onto the selected eigenvectors; and selecting documents having significant importance based on said calculated weighted sum of square projections of the document segment vectors; wherein the vector d n after the projection is represented by z n =(z n1 , z n2 , . . . z nL ), a projection value of d n to a m -th eigenvector is given by z nm =φ m t d n , where φ m represents the m -th eigenvector and t represents transpose; a sum of squared projections onto a L dimensional subspace being given by: Σ m L =IZ nm 2 or Σ m L =Iλ m z nm 2 , where λ m represents the eigenvalue of the m -th eigenvector”.