9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Identifying Relevant Phrases to Summarize Decisions in Spoken Meetings

Raquel Fernandez (1), Matthew Frampton (1), John Dowding (2), Anish Adukuzhiyil (1), Patrick Ehlen (1), Stanley Peters (1)

(1) Stanford University, USA; (2) University of California at Santa Cruz, USA

We address the problem of identifying words and phrases that accurately capture, or contribute to, the semantic gist of decisions made in multi-party human-human meetings. We first describe our approach to modelling decision discussions in spoken meetings and then compare two approaches to extracting information from these discussions. The first one uses an open-domain semantic parser that identifies candidate phrases for decision summaries and then employs machine learning techniques to select from those candidate phrases. The second one uses categorical and sequential classifiers that exploit simple syntactic and semantic features to identify words and phrases relevant for decision summarization.

Full Paper

Bibliographic reference.  Fernandez, Raquel / Frampton, Matthew / Dowding, John / Adukuzhiyil, Anish / Ehlen, Patrick / Peters, Stanley (2008): "Identifying relevant phrases to summarize decisions in spoken meetings", In INTERSPEECH-2008, 78-81.