We develop an abstractive summarization framework independent of labeled data
for multiple heterogeneous documents. Unlike existing multi-document
summarization methods, our framework processes documents telling different
stories instead of documents on the same topic. We also enhance an existing
sentence fusion method with a uni-directional language model to prioritize
fused sentences with higher sentence probability with the goal of increasing
readability. Lastly, we construct a total of twelve dataset variations based on
CNN/Daily Mail and the NewsRoom datasets, where each document group contains a
large and diverse collection of documents to evaluate the performance of our
model in comparison with other baseline systems. Our experiments demonstrate
that our framework outperforms current state-of-the-art methods in this more
generic setting.

开发了一种基于抽象的总结框架，适用于多个异构文档，该框架独立于标记数据。 与现有的多文件总结方法不同，我们的框架处理讲述不同故事的文档，而不是同一主题的文档。 最后，我们基于 CNN / Daily Mail 和 NewsRoom 数据集构建了总共十二个数据集变体，其中每个文档组都包含大量且多样化的文档，以评估我们的模型与其他基线系统的性能。 我们的实验表明，我们的框架在这种更通用的情况下胜过了现有的最先进方法。