Unsupervised Sentence Compression using Denoising Auto-Encoders
This addresses the problem of needing large paired corpora for sentence compression, offering an unsupervised alternative that is incremental in performance.
The paper tackled sentence compression without paired training data by using denoising auto-encoders to add noise and recover original sentences, showing that unsupervised models perform comparably to supervised baselines in human evaluations for grammatical correctness and meaning retention.
In sentence compression, the task of shortening sentences while retaining the original meaning, models tend to be trained on large corpora containing pairs of verbose and compressed sentences. To remove the need for paired corpora, we emulate a summarization task and add noise to extend sentences and train a denoising auto-encoder to recover the original, constructing an end-to-end training regime without the need for any examples of compressed sentences. We conduct a human evaluation of our model on a standard text summarization dataset and show that it performs comparably to a supervised baseline based on grammatical correctness and retention of meaning. Despite being exposed to no target data, our unsupervised models learn to generate imperfect but reasonably readable sentence summaries. Although we underperform supervised models based on ROUGE scores, our models are competitive with a supervised baseline based on human evaluation for grammatical correctness and retention of meaning.