LSA works by first separating text into sentences, then building a matrix of word counts in each sentence. It normalizes the matrix using tf-idf to weigh common words lower. SVD transforms the matrix into a conceptual space, where each sentence is represented as a vector. The top sentences are picked based on the absolute values of their vectors in this space.