Lexical cohesion based topic modeling for summarization

Date

2008-02

Editor(s)

Advisor

Supervisor

Co-Advisor

Co-Supervisor

Instructor

BUIR Usage Stats
0
views
24
downloads

Citation Stats

Series

Abstract

In this paper, we attack the problem of forming extracts for text summarization. Forming extracts involves selecting the most representative and significant sentences from the text. Our method takes advantage of the lexical cohesion structure in the text in order to evaluate significance of sentences. Lexical chains have been used in summarization research to analyze the lexical cohesion structure and represent topics in a text. Our algorithm represents topics by sets of co-located lexical chains to take advantage of more lexical cohesion clues. Our algorithm segments the text with respect to each topic and finds the most important topic segments. Our summarization algorithm has achieved better results, compared to some other lexical chain based algorithms. © 2008 Springer-Verlag Berlin Heidelberg.

Source Title

International Conference on Intelligent Text Processing and Computational Linguistics CICLing 2008: Computational Linguistics and Intelligent Text Processing

Publisher

Springer

Course

Other identifiers

Book Title

Degree Discipline

Degree Level

Degree Name

Citation

Published Version (Please cite this version)

Language

English