  This paper addresses the problem of summarizing decisions in spoken meetings:
our goal is to produce a concise {\it decision abstract} for each meeting
decision. We explore and compare token-level and dialogue act-level automatic
summarization methods using both unsupervised and supervised learning
frameworks. In the supervised summarization setting, and given true clusterings
of decision-related utterances, we find that token-level summaries that employ
discourse context can approach an upper bound for decision abstracts derived
directly from dialogue acts. In the unsupervised summarization setting,we find
that summaries based on unsupervised partitioning of decision-related
utterances perform comparably to those based on partitions generated using
supervised techniques (0.22 ROUGE-F1 using LDA-based topic models vs. 0.23
using SVMs).
