Term-Weighting for Summarization of Multi-party Spoken Dialogues

Gabriel Murray, Steve Renals

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationMachine Learning for Multimodal Interaction
Subtitle of host publication4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007, Revised Selected Papers
EditorsAndrei Popescu-Belis, Steve Renals, Hervé Bourlard
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Number of pages12
ISBN (Electronic)978-3-540-78155-4
ISBN (Print)978-3-540-78154-7
Publication statusPublished - 2008
Event4th International Workshop MLMI 2007 - Brno, Czech Republic
Duration: 28 Jun 200730 Jun 2007

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


Workshop4th International Workshop MLMI 2007
CountryCzech Republic

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