Summarizing Decisions in Spoken Meetings
نویسندگان
چکیده
This paper addresses the problem of summarizing decisions in spoken meetings: our goal is to produce a concise 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 decisionrelated 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).
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ورودعنوان ژورنال:
- CoRR
دوره abs/1606.07965 شماره
صفحات -
تاریخ انتشار 2011