Actions
  • shareshare
  • link
  • cite
  • add
add
auto_awesome_motion View all 1 versions
Publication . Article . 2004

SeLeCT: a lexical cohesion based news story segmentation system

Stokes, N.; Carthy, J.; Alan F. Smeaton;
Open Access
English
Published: 01 Jan 2004
Publisher: IOS Press
Country: Ireland
Abstract

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.

Subjects by Vocabulary

ACM Computing Classification System: InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL

Subjects

Artificial intelligence, Digital video, Algorithms

25 references, page 1 of 3

[1] J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang. Topic detection and tracking pilot study: Final report. In the Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, 1998.

[2] R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In the Proceedings of the Intelligent Scalable Text Summarization Workshop, 1997. [OpenAIRE]

[3] D. Beeferman, A. Berger, and J. Lafferty. Statistical models for text segmentation. Machine Learning, 34(1- 3):177-210, 1999.

[4] F. Choi. Advances in domain independent linear text segmentation. In the Proceedings of the North American Chapter of the ACL, 2000.

[5] S. Dharanipragada, M. Franz, J.S. McCarley, S. Roukos, and T. Ward. Story segmentation and topic detection. In the Proceedings of the DARPA Broadcast News Workshop, 1999. [OpenAIRE]

[6] M.A.K. Halliday. Spoken and Written Language. Oxford University Press, 1985.

[7] M.A.K. Halliday and R. Hasan. Cohesion in English. Longman, 1976.

[8] M. Hearst. Texttiling: Segmenting text into multiparagraph subtopic passages. Computational Linguistics, 23(1):33-64, 1997.

[9] J.S. Justeson and S.M. Katz. Technical terminology: some linguistic properties and an algorithm for identification in text. Natural Language Engineering, (11):9- 27, 1995.

[10] H. Kozima. Text segmentation based on similarity between words. In the Proceedings of the Association for Computational Linguistics, pages 286-288, 1993. [OpenAIRE]

moresidebar