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    <abstract>In this article we address the task of automatic text structuring into linear and non-overlapping thematic episodes. Our investigation reports on the use of various lexical, acoustic and syntactic features, and makes a comparison of how these features influence performance of automatic topic segmentation. Using datasets containing multi-party meeting transcriptions, we base our experiments on a proven state-of-the-art approach using support vector classification.</abstract>
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%0 Conference Proceedings
%T Exploiting structural meeting-specific features for topic segmentation
%A Georgescul, Maria
%A Clarck, Alexander
%A Armstrong, Susan
%S Actes de la 14ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs
%D 2007
%8 jun
%I ATALA
%C Toulouse, France
%F georgescul-etal-2007-exploiting
%X In this article we address the task of automatic text structuring into linear and non-overlapping thematic episodes. Our investigation reports on the use of various lexical, acoustic and syntactic features, and makes a comparison of how these features influence performance of automatic topic segmentation. Using datasets containing multi-party meeting transcriptions, we base our experiments on a proven state-of-the-art approach using support vector classification.
%U https://aclanthology.org/2007.jeptalnrecital-long.1
%P 15-24

Markdown (Informal)

[Exploiting structural meeting-specific features for topic segmentation](https://aclanthology.org/2007.jeptalnrecital-long.1) (Georgescul et al., JEP/TALN/RECITAL 2007)

• Exploiting structural meeting-specific features for topic segmentation (Georgescul et al., JEP/TALN/RECITAL 2007)

ACL

• Maria Georgescul, Alexander Clarck, and Susan Armstrong. 2007. Exploiting structural meeting-specific features for topic segmentation. In Actes de la 14ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs, pages 15–24, Toulouse, France. ATALA.