Paraphrasing Headlines by Machine Translation : Sentential paraphrase acquisition and generation using Google News

Abstract

In this paper we investigate the automatic collection, generation and evaluation of sentential paraphrases. Valuable sources of paraphrases are news article headlines; they tend to describe the same event in various different ways, and can easily be obtained from the web. We describe a method for generating paraphrases by using a large aligned monolingual corpus of news headlines acquired automatically from Google News and a standard Phrase-Based Machine Translation (PBMT) framework. The output of this system is compared to a word substitution baseline. Human judges prefer the PBMT paraphrasing system over the word substitution system. We compare human judgements to automatic judgement measures and demonstrate that the BLEU metric correlates well with human judgements provided that the generated paraphrase is sufficiently different from the source sentence.