Machine learning of generic and user-focused summarization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Efficient text summarization using lexical chains
Proceedings of the 5th international conference on Intelligent user interfaces
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
Text summarization using a trainable summarizer and latent semantic analysis
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Computer Speech and Language
Experimentation of Two Compression Strategies for Multi-document Summarization
ICCEE '09 Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 02
Genetic algorithm based multi-document summarization
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A new approach to improving multilingual summarization using a genetic algorithm
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Graph based single document summarization
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
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Learners with reading difficulties normally face significant challenges in understanding the text-based learning materials. In this regard, there is a need for an assistive summary to help such learners to approach the learning documents with minimal difficulty. An important issue in extractive summarization is to extract cohesive summary from the text. Existing summarization approaches focus mostly on informative sentences rather than cohesive sentences. We considered several existing features, including sentence location, cardinality, title similarity, and keywords to extract important sentences. Moreover, learner-dependent readability-related features such as average sentence length, percentage of trigger words, percentage of polysyllabic words, and percentage of noun entity occurrences are considered for the summarization purpose. The objective of this work is to extract the optimal combination of sentences that increase readability through sentence cohesion using genetic algorithm. The results show that the summary extraction using our proposed approach performs better in F-measure, readability, and cohesion than the baseline approach (lead) and the corpus-based approach. The task-based evaluation shows the effect of summary assistive reading in enhancing readability on reading difficulties.