Term-Weighting for Summarization of Multi-party Spoken Dialogues

This paper explores the issue of term-weighting in the genre of spontaneous, multi-party spoken dialogues, with the intent of using such term-weights in the creation of extractive meeting summaries. The field of text information retrieval has yielded many term-weighting techniques to import for our purposes; this paper implements and compares several of these, namely TF · IDF, Residual IDF and Gain. We propose that term-weighting for multi-party dialogues can exploit patterns in word usage among participant speakers, and introduce the SU · IDF metric as one attempt to do so. Results for all metrics are reported on both manual and automatic speech recognition (ASR) transcripts, and on both the ICSI and AMI meeting corpora.

• Type: Conference
• Year: 2007
• Where: MLMI
• Authors: Gabriel Murray, Steve Renals
• Keywords: Extractive Meeting Summaries, Machine Learning, MLMI 2007, Multi-party Spoken Dialogues, Term-weighting Techniques