%0 Journal Article
%T Using AdaBoost Meta-Learning Algorithm for Medical News Multi-Document Summarization
%A Mahdi Gholami Mehr
%J Intelligent Information Management
%P 182-190
%@ 2160-5920
%D 2013
%I Scientific Research Publishing
%R 10.4236/iim.2013.56020
%X <p class=\"MsoNormal\">
	<span lang=\"EN-US\" style=\"font-family:Verdana;\">Automatic text summarization involves reducing a text document or a
larger corpus of multiple documents</span><span lang=\"EN-US\"> </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">to a short set of sentences or paragraph</span><span lang=\"EN-US\" style=\"font-family:Verdana;\">s</span><span lang=\"EN-US\" style=\"font-family:Verdana;\"> that
convey</span><span lang=\"EN-US\"> </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">the main meaning of the text. In this paper</span><span lang=\"EN-US\" style=\"font-family:Verdana;\">, </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">we discuss about
multi-document summarization that differs from </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">the </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">single</span><span lang=\"EN-US\" style=\"font-family:Verdana;\"> one</span><span lang=\"EN-US\" style=\"font-family:Verdana;\"> in </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">which</span><span lang=\"EN-US\" style=\"font-family:Verdana;\"> the
issues of compression, speed, redundancy and passage selection are critical in
the formation of useful summaries. Since the number and variety of online
medical news make them difficult for experts in </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">the </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">medical field to
read all of the medical news, an automatic multi-document summarization can be
useful for easy study of information on the web. Hence we propose a new approach
based on machine learning meta-learner algorithm called AdaBoost that is used
for summarization. We treat a document as a set of sentences, </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">and</span><span lang=\"EN-US\" style=\"font-family:Verdana;\"> the
learning algorithm must learn to classify as positive or negative examples of
sentences based on </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">the </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">score of the sentences. For this learning task</span><span lang=\"EN-US\" style=\"font-family:Verdana;\">, </span><span lang=\"EN-US\" style=\"font-family:Verdana;\">we
apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been
chosen as the base learner. In our experiment</span><span lang=\"EN-US\" style=\"font-family:Verdana;\">, </span><span lang=\"EN-US\"><span style=\"font-family:Verdana;\">we use </span><a name=\"OLE_LINK20\"></a><span
%K Multi-Document Summarization
%K Machine Learning
%K Decision Trees
%K AdaBoost
%K C4.5
%K Medical Document Summarization
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=40201