  We present a novel unsupervised framework for focused meeting summarization
that views the problem as an instance of relation extraction. We adapt an
existing in-domain relation learner (Chen et al., 2011) by exploiting a set of
task-specific constraints and features. We evaluate the approach on a decision
summarization task and show that it outperforms unsupervised utterance-level
extractive summarization baselines as well as an existing generic
relation-extraction-based summarization method. Moreover, our approach produces
summaries competitive with those generated by supervised methods in terms of
the standard ROUGE score.
