Two different approaches have been proposed for Sentence Compression: purely statistical methodologies<cite> (Barzilay & Lee, 2003</cite>; Le Nguyen & Ho, 2004) and hybrid linguistic/statistic methodologies (Knight & Marcu, 2002; Shinyama et al., 2002; Daelemans et al., 2004; Marsi & Krahmer, 2005; Unno et al., 2006) . As our work is based on the first paradigm, we will focus on the works proposed by<cite> (Barzilay & Lee, 2003)</cite> and (Le Nguyen & Ho, 2004) . (Barzilay & Lee, 2003 ) present a knowledge-lean algorithm that uses multiple-sequence alignment to learn generate sentence-level paraphrases essentially from unannotated corpus data alone. In contrast to (Barzilay & Lee, 2002) , they need neither parallel data nor explicit information about sentence semantics. Rather, they use two comparable corpora. Their approach has three main steps. First, working on each of the comparable corpora separately, they compute lattices compact graph-based representations to find commonalities within groups of structurally similar sentences. Next, they identify pairs of lattices from the two different corpora that are paraphrases of each other. Finally, given an input sentence to be paraphrased, they match it to a lattice and use a paraphrase from the matched lattices mate to generate an output sentence.