Sentence Compression with Joint Structural Inference

Sentence compression techniques often assemble output sentences using fragments of lexical sequences such as ngrams or units of syntactic structure such as edges from a dependency tree representation. We present a novel approach for discriminative sentence compression that unifies these notions and jointly produces sequential and syntactic representations for output text, leveraging a compact integer linear programming formulation to maintain structural integrity. Our supervised models permit rich features over heterogeneous linguistic structures and generalize over previous state-of-theart approaches. Experiments on corpora featuring human-generated compressions demonstrate a 13-15% relative gain in 4gram accuracy over a well-studied language model-based compression system.