  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.
