Fuzzy-Rough Set Aided Sentence Extraction Summarization
- 24 October 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 450-453
- https://doi.org/10.1109/icicic.2006.90
Abstract
In this paper, a novel method is proposed to extract key sentences of a document as its summary by estimating the relevance of sentences through the use of fuzzy-rough sets. This method uses senses rather than raw words to lessen the problem that sentences of the same or similar semantic meaning but written in synonyms are treated differently. Also included is semantic clustering, used to avoid selecting redundant key sentences. A prototype of this automatic text summarization scheme is constructed and an intrinsic method with criteria widely used in information-retrieval systems is employed for measuring the summary quality. The results of applying the prototype to datasets with manually-generated summaries are shownKeywords
This publication has 10 references indexed in Scilit:
- Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approachesIEEE Transactions on Knowledge and Data Engineering, 2004
- Case generation using rough sets with fuzzy representationIEEE Transactions on Knowledge and Data Engineering, 2004
- Mining stock price using fuzzy rough set systemExpert Systems with Applications, 2003
- Measures of ruggedness using fuzzy-rough sets and fractals: applications in medical time seriesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- SUMMAC: a text summarization evaluationNatural Language Engineering, 2002
- An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNetLecture Notes in Computer Science, 2002
- The challenges of automatic summarizationComputer, 2000
- Extracting sentence segments for text summarizationPublished by Association for Computing Machinery (ACM) ,2000
- Putting Rough Sets and Fuzzy Sets TogetherPublished by Springer Science and Business Media LLC ,1992
- Constructing literature abstracts by computer: Techniques and prospectsInformation Processing & Management, 1990