A relevance feedback model for fractal summarization

Fu Lee Wang, Christopher C. Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

As a result of the recent information explosion, there is an increasing demand for automatic summarization, and human abstractors often synthesize summaries that are based on sentences that have been extracted by machine. However, the quality of machine-generated summaries is not high. As a special application of information retrieval systems, the precision of automatic summarization can be improved by user relevance feedback, in which the human abstractor can direct the sentence extraction process and useful information can be retrieved efficiently. Automatic summarization with relevance feedback is a helpful tool to assist professional abstractors in generating summaries, and in this work we propose a relevance feedback model for fractal summarization. The results of the experiment show that relevance feedback effectively improves the performance of automatic fractal summarization.

Original languageEnglish
Title of host publicationDigital Libraries
Subtitle of host publicationInternational Collaboration and Cross-Fertilization - 7th International Conference on Asian Digital Libraries, ICADL 2004
EditorsQihao Miao, Ee-peng Lim, Zhaoneng Chen, Yuxi Fu, Hsinchun Chen, Edward Fox
Pages368-377
Number of pages10
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event7th International Conference on Asian Digital Libraries, ICADL 2004 - Shanghai, China
Duration: 13 Dec 200417 Dec 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3334 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Asian Digital Libraries, ICADL 2004
Country/TerritoryChina
CityShanghai
Period13/12/0417/12/04

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