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SeLeCT: a lexical cohesion based news story segmentation system

Stokes, Nicola and Carthy, Joe and Smeaton, Alan F. (2004) SeLeCT: a lexical cohesion based news story segmentation system. AI Communications, 17 (1). pp. 3-12. ISSN 0921-7126

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In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between textual units using a linguistic technique called lexical chaining. We evaluate the relative performance of SeLeCT with respect to two other cohesion based segmenters: TextTiling and C99. Using a recently introduced evaluation metric WindowDiff, we contrast the segmentation accuracy of each system on both "spoken" (CNN news transcripts) and "written" (Reuters newswire) news story test sets extracted from the TDT1 corpus.

Item Type:Article (Published)
Uncontrolled Keywords:Lexical Cohesion; Lexical Chaining; Text Segmentation; NLP;
Subjects:Computer Science > Artificial intelligence
Computer Science > Digital video
Computer Science > Algorithms
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:IOS Press
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Enterprise Ireland
ID Code:203
Deposited On:04 Mar 2008 by DORAS Administrator. Last Modified 06 May 2010 15:37

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