Journal ArticleDOI
Summarising text with a genetic algorithm-based sentence extraction
TLDR
These factors are introduced and the Genetic Algorithm with the specific fitness function is discussed, and once the summary is created, it is evaluated using a fitness function.Abstract:
Automatic text summarisation has long been studied and used. The growth in the amount of information on the web results in more demands for automatic methods for text summarisation. Designing a system to produce human-quality summaries is difficult and therefore, many researchers have focused on sentence or paragraph extraction, which is a kind of summarisation. In this paper, we introduce a new method to make such extracts. Genetic Algorithm (GA)-based sentence selection is used to make a summary, and once the summary is created, it is evaluated using a fitness function. The fitness function is based on three following factors: Readability Factor (RF), Cohesion Factor (CF) and Topic-Relation Factor (TRF). In this paper, we introduce these factors and discuss the Genetic Algorithm with the specific fitness function. Evaluation results are also shown and discussed in the paper.read more
Citations
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Journal ArticleDOI
Content-Based Citation Analysis: The Next Generation of Citation Analysis
TL;DR: This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications, and highlights how increased computational capabilities and publicly available full‐text resources have opened this area of research to vast possibilities.
Journal ArticleDOI
Extractive single-document summarization based on genetic operators and guided local search
TL;DR: This paper proposes a method of extractive single-document summarization based on genetic operators and guided local search, called MA-SingleDocSum, which was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking and showed that MA- singleDocSum outperforms the state-of-the-art methods.
Journal ArticleDOI
Fuzzy evolutionary cellular learning automata model for text summarization
TL;DR: A new model for automatic text summarization is introduced which is based on fuzzy logic system, evolutionary algorithms and cellular learning automata, and a new approach is proposed to adjust the best weights of the text features using particle swarm optimization and genetic algorithm.
Journal ArticleDOI
A Hybrid Approach for Arabic Text Summarization Using Domain Knowledge and Genetic Algorithms
TL;DR: The (ASDKGA) approach demonstrated promising results when summarizing Arabic political documents with average F-measure of 0.605 at the compression ratio of 40%.
Journal ArticleDOI
Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment
TL;DR: A machine learning-based approach to summarize user's opinion expressed in reviews using sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification using a unified feature set to design a more accurate classification system.
References
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Gerard Salton,Chris Buckley +1 more
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Proceedings ArticleDOI
A trainable document summarizer
TL;DR: The trends in the results are in agreement with those of Edmundson who used a subjectively weighted combination of features as opposed to training the feature weights using a corpus, which suggests that even shorter extracts may be useful indicative summmies.