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Journal of Intelligent Information Systems

Erschienen in:

06.01.2016

A just-in-time keyword extraction from meeting transcripts using temporal and participant information

verfasst von: Hyun-Je Song, Junho Go, Seong-Bae Park, Se-Young Park, Kweon Yang Kim

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 1/2017

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Abstract

In a meeting, it is often desirable to extract the keywords from each utterance as soon as it is spoken. Therefore, this paper proposes a just-in-time keyword extraction from meeting transcripts. The proposed method considers three major factors that make it different from keyword extraction from normal texts. The first factor is the temporal history of the preceding utterances that grants higher importance to recent utterances than older ones, and the second is topic relevance, which focuses only on the preceding utterances relevant to the current utterance. The final factor is the participants. The utterances spoken by the current speaker should be considered more important than those spoken by other participants. The proposed method considers these factors simultaneously under a graph-based keyword extraction with some graph operations. Experiments on two data sets in English and Korean show that consideration of these factors results in improved performance in keyword extraction from meeting transcripts.

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This data set is available at http://ml.knu.ac.kr/dataset/keywordextraction.html.

A guideline was given to the annotators that keywords must be a single word and the maximum number of keywords per utterance is five.

The instructions how to segment a meeting into several topic segments are given at http://groups.inf.ed.ac.uk/ami/corpus/Guidelines/TopicSegmentationGuidelinesNonScenario.pdf

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Titel: A just-in-time keyword extraction from meeting transcripts using temporal and participant information
verfasst von: Hyun-Je Song
Junho Go
Seong-Bae Park
Se-Young Park
Kweon Yang Kim
Publikationsdatum: 06.01.2016
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 1/2017
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-015-0391-2

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