Abstract:
Automatic text summarization is the process of creating a small version from the original
text. Extraction approach is one of way of extracting the most important sentences in document,
this approach is used to select sentences after calculating the score for each sentence, and based
on user defined summary ratio the top n sentences are selected as summary. The selection of the
informative sentence is a challenge for extraction based automatic text summarization
researchers. This research applied extraction based automatic single document text
summarization method using the particle swarm optimization algorithm to find the best feature
weight score to differentiate between important and non important feature. The Recall-Oriented
Understanding for Gisting Evaluation (ROUGE) toolkit was used for measuring
performance. DUC 2002 data sets provided by the Document Understanding Conference
2002 were used in the evaluation process. The summary that generated by PSO algorithm was
compared with other algorithm (GA,ACO) and used DE algorithm as benchmark. Experimental
results showed that the summaries produced by the DE algorithm are better than another
algorithm
