This paper presents a tree-to-tree transduction method for sentence compression. Our model is based on synchronous tree substitution grammar, a formalism that allows local distortion of the tree topology and can thus naturally capture structural mismatches. We describe an algorithm for decoding in this framework and show how the model can be trained discriminatively within a large margin framework. Experimental results on sentence compression bring significant improvements over a state-of-the-art model.
A different approach was used by REF , that experimented a tree-to-tree transduction method for sentence compression.
Sentence Compression as Tree Transduction
Journal Of Artificial Intelligence Research, Volume 34, pages 637-674, 2009
Computer Science