Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic condensation of electronic publications by sentence selection
Information Processing and Management: an International Journal - Special issue: summarizing text
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Modern Information Retrieval
Automated text summarization and the SUMMARIST system
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Introduction to Information Retrieval
Introduction to Information Retrieval
Extractive single-document summarization based on genetic operators and guided local search
Expert Systems with Applications: An International Journal
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Currently vast amounts of textual information exist in large repositories such as Web. To processes such a huge amount of information, automatic text summarization has been of great interests. Unlike many approaches which focus on sentence or paragraph extraction, in this research, we introduce a method to make extractions based on three factors of Readability, Cohesion and Topic relation. We use Harmony Search-based sentence selection to make such a summary. Once the summary is created, it is evaluated using a fitness function based on those three factors. The evaluation of the algorithm on a test collection is also presented in the paper. Our results indicate that the extracted summaries by our proposed scheme have better precision and recall than the other approaches.