Information access in complex, poorly structured information spaces
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Introduction to the special issue on summarization
Computational Linguistics - Summarization
Graph-based ranking algorithms for sentence extraction, applied to text summarization
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
The automatic creation of literature abstracts
IBM Journal of Research and Development
Machine-made index for technical literature: an experiment
IBM Journal of Research and Development
Use of genetic algorithm for cohesive summary extraction to assist reading difficulties
Applied Computational Intelligence and Soft Computing
Extractive single-document summarization based on genetic operators and guided local search
Expert Systems with Applications: An International Journal
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E-Learning aims at defining education to be made as anytime, anywhere and anybody entity. Usability can be increased by incorporating summarization in E-learning context. The aim of the text summarization is to select the most important information from an abundance of text. This paper investigates a new approach for single document summarization based on graph traversal technique with constraint to improve cohesion. The selection of features plays a vital role in the sentence extraction. By considering both the structured and the unstructured features, better summary can be generated.