Neural diverse abstractive sentence compression generation

Abstract

In this work, we have contributed a novel abstractive sentence compression model which generates diverse compressed sentence with paraphrase using a neural seq2seq encoder decoder model. We impose several operations in order to generate diverse abstractive compressions at the sentence level which was not addressed in the past research works. Our model jointly improves the information coverage and abstractiveness of the generated sentences. We conduct our experiments on the human-generated abstractive sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods across different metrics.