  Highlighting is a powerful tool to pick out important content and emphasize.
Creating summary highlights at the sub-sentence level is particularly
desirable, because sub-sentences are more concise than whole sentences. They
are also better suited than individual words and phrases that can potentially
lead to disfluent, fragmented summaries. In this paper we seek to generate
summary highlights by annotating summary-worthy sub-sentences and teaching
classifiers to do the same. We frame the task as jointly selecting important
sentences and identifying a single most informative textual unit from each
sentence. This formulation dramatically reduces the task complexity involved in
sentence compression. Our study provides new benchmarks and baselines for
generating highlights at the sub-sentence level.
