  Automatic sentence summarization produces a shorter version of a sentence,
while preserving its most important information. A good summary is
characterized by language fluency and high information overlap with the source
sentence. We model these two aspects in an unsupervised objective function,
consisting of language modeling and semantic similarity metrics. We search for
a high-scoring summary by discrete optimization. Our proposed method achieves a
new state-of-the art for unsupervised sentence summarization according to ROUGE
scores. Additionally, we demonstrate that the commonly reported ROUGE F1 metric
is sensitive to summary length. Since this is unwillingly exploited in recent
work, we emphasize that future evaluation should explicitly group summarization
systems by output length brackets.
