Actions
  • shareshare
  • link
  • cite
  • add
add
auto_awesome_motion View all 2 versions
Publication . Book . Part of book or chapter of book . 2004

Broadcast news gisting using lexical cohesion analysis

Nicola Stokes; Eamonn Newman; Joe Carthy; Alan F. Smeaton;
Open Access
Published: 01 Jan 2004
Country: Ireland
Abstract

In this paper we describe an extractive method of creating very short summaries or gists that capture the essence of a news story using a linguistic technique called lexical chaining. The recent interest in robust gisting and title generation techniques originates from a need to improve the indexing and browsing capabilities of interactive digital multimedia systems. More specifically these systems deal with streams of continuous data, like a news programme, that require further annotation before they can be presented to the user in a meaningful way. We automatically evaluate the performance of our lexical chaining-based gister with respect to four baseline extractive gisting methods on a collection of closed caption material taken from a series of news broadcasts. We also report results of a human-based evaluation of summary quality. Our results show that our novel lexical chaining approach to this problem outperforms standard extractive gisting methods.

Subjects by Vocabulary

Microsoft Academic Graph classification: Search engine indexing Closed captioning Lexical analysis Cohesion (linguistics) Multimedia computer.software_genre computer Natural language processing Annotation Artificial intelligence business.industry business Chaining Proper noun Computer science

Subjects

Digital video, Information retrieval

33 references, page 1 of 4

Smeaton A.F., H. Lee, N. O'Connor, S Marlow, N. Murphy, TV News Story Segmentation, Personalisation and Recommendation. AAAI 2003 Spring Symposium on Intelligent Multimedia Knowledge Management, Stanford University 24-26 March 2003.

3. Witbrock, M., V. Mittal, Ultra-Summarisation: A Statistical approach to generating highly condensed non-extractive summaries. In the Proceedings of the ACM-SIGIR, pp. 315- 316, 1999.

4. Morris J., G. Hirst, Lexical Cohesion by Thesaural Relations as an Indicator of the Structure of Text, Computational Linguistics 17(1), 1991.

5. Halliday M.A.K., Spoken and Written Language. Oxford University Press, 1985.

6. Green S.J., Automatically Generating Hypertext By Comparing Semantic Similarity. University of Toronto, Technical Report number 366, October 1997.

7. Barzilay R., M. Elhadad, Using Lexical Chains for Text Summarization. In the proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97), ACL, 1997. [OpenAIRE]

8. Silber G.H., Kathleen F. McCoy, Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization. Computational Linguistics 28(4): 487- 496, 2002.

9. Fuentes M., H. Rodriguez, L. Alonso, Mixed Approach to Headline Extraction for DUC 2003. In the Proceedings of the HLT/NAACL workshop on Automatic Summarization/Document Understanding Conference (DUC 2003), 2003.

10. Chali, Y., M. Kolla, N. Singh, Z. Zhang, The University of Lethbridge Text Summarizer at DUC 2003. In the Proceedings of the HLT/NAACL workshop on Automatic Summarization/Document Understanding Conference (DUC 2003), 2003.

11. St-Onge D., Detecting and Correcting Malapropisms with Lexical Chains, Dept. of Computer Science, University of Toronto, M.Sc. Thesis, 1995.

Related to Research communities
Download fromView all 3 sources
lock_open
DCU Online Research Access Service
Part of book or chapter of book . Conference object . 2004
License: cc-by-nc-sa
moresidebar