In this paper we generalise the sentence compression task. Rather than simply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. We present a new corpus that is suited to our task and a discriminative tree-totree transduction model that can naturally account for structural and lexical mismatches. The model incorporates a novel grammar extraction method, uses a language model for coherent output, and can be easily tuned to a wide range of compression specific loss functions.
REF proposed a discriminative tree-to-tree transduction model which incorporated a grammar extraction method and used a language model for coherent output.
Sentence Compression Beyond Word Deletion
International Conference On Computational Linguistics
Computer Science