A term weighting method based on lexical chain for automatic summarization

Young In Song, Kyoung Soo Han, Hae Chang Rim

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Citations (Scopus)

Abstract

We suggest a new term weighting method based on lexical cohesion in a text. To compute cohesion, we use lexical chain with a new lexical chain disambiguation method considering association between words and characteristics of WordNet. In our experiment, the methods show a better result than traditional term weighting methods such as tf and tf.idf.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAlexander Gelbukh
PublisherSpringer Verlag
Pages636-639
Number of pages4
ISBN (Print)3540210067, 9783540210061
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2945
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'A term weighting method based on lexical chain for automatic summarization'. Together they form a unique fingerprint.

  • Cite this

    Song, Y. I., Han, K. S., & Rim, H. C. (2004). A term weighting method based on lexical chain for automatic summarization. In A. Gelbukh (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 636-639). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2945). Springer Verlag. https://doi.org/10.1007/978-3-540-24630-5_78