Sentence fusion is the task of joining several independent sentences into a
single coherent text. Current datasets for sentence fusion are small and
insufficient for training modern neural models. In this paper, we propose a
method for automatically-generating fusion examples from raw text and present
DiscoFuse, a large scale dataset for discourse-based sentence fusion. We author
a set of rules for identifying a diverse set of discourse phenomena in raw
text, and decomposing the text into two independent sentences. We apply our
approach on two document collections: Wikipedia and Sports articles, yielding
60 million fusion examples annotated with discourse information required to
reconstruct the fused text. We develop a sequence-to-sequence model on
DiscoFuse and thoroughly analyze its strengths and weaknesses with respect to
the various discourse phenomena, using both automatic as well as human
evaluation. Finally, we conduct transfer learning experiments with WebSplit, a
recent dataset for text simplification. We show that pretraining on DiscoFuse
substantially improves performance on WebSplit when viewed as a sentence fusion
task.

本文介绍了一种基于程式的方法，并以 DiscoFuse 数据集来训练序列到序列模型，针对常见的文本磨合现象及转移学习的效果进行分析。