10. The method of claim 9 , wherein segmenting the given communication transcript into discrete segments is performed by assigning to each sentence of the given communication transcript a segment label that corresponds to the discrete segment cluster into which the sentence type assigned to the sentence is grouped, and then using an agglomerative hierarchical clustering method comprising: merging sequential sentences of the given communication transcript to which the same segment label has been assigned to form a sequence of discrete sentence segments; and successively merging the pair of adjacent sentence segments in the sequence of discrete sentence segments for which the merger forms a sentence segment having a maximum purity value among sentence segments that would be formed by mergers of every pair of adjacent sentence segments in the sequence of discrete sentence segments until the maximum purity value is less than a specified threshold.
10. The method of claim 9 , wherein segmenting the given communication transcript into discrete segments is performed by assigning to each sentence of the given communication transcript a segment label that corresponds to the discrete segment cluster into which the sentence type assigned to the sentence is grouped, and then using an agglomerative hierarchical clustering method comprising: merging sequential sentences of the given communication transcript to which the same segment label has been assigned to form a sequence of discrete sentence segments; and successively merging the pair of adjacent sentence segments in the sequence of discrete sentence segments for which the merger forms a sentence segment having a maximum purity value among sentence segments that would be formed by mergers of every pair of adjacent sentence segments in the sequence of discrete sentence segments until the maximum purity value is less than a specified threshold. 11. The method of claim 10 , wherein the purity value of a sentence segment is determined based upon a probability of the sentence segment being a non-noisy occurrence.