Monolingual Distributional Similarity for Text-to-Text Generation

Previous work on paraphrase extraction and application has relied on either parallel datasets, or on distributional similarity metrics over large text corpora. Our approach combines these two orthogonal sources of information and directly integrates them into our paraphrasing system’s log-linear model. We compare different distributional similarity feature-sets and show significant improvements in grammaticality and meaning retention on the example text-to-text generation task of sentence compression, achieving stateof-the-art quality.