Using Clustering and Modified Classification algorithm without a learning corpus for automatic text summarization

Simple item page
dc.contributor.authorAries, Abdelkrime
dc.contributor.authorOufaida, Houda
dc.contributor.authorNouali, Omar
dc.date.accessioned2015-05-13T16:02:12Z
dc.date.available2015-05-13T16:02:12Z
dc.date.issued2013-02-05
dc.description.abstractIn this paper we describe a modified classification method destined for extractive summarization purpose. The classification in this method doesn’t need a learning corpus; it uses the input text to do that. First, we cluster the document sentences to exploit the diversity of topics, then we use a learning algorithm (here we used Naive Bayes) on each cluster considering it as a class. After obtaining the classification model, we calculate the score of a sentence in each class, using a scoring model derived from classification algorithm. These scores are used, then, to reorder the sentences and extract the first ones as the output summary. We conducted some experiments using a corpus of scientific papers, and comparing our system to another system which is UNIS system. Also, we experiment the impact of clustering threshold tuning, on the resulted summary, as well as the impact of adding more features to the classifier. We found that this method is interesting, and gives good performance, and the addition of new features (which is simple using this method) can improve summary’s accuracy.fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/747
dc.relation.ispartofThe 20th international conference on document recognation and retrieval DRRfr_FR
dc.relation.placeSan Fransisco California USAfr_FR
dc.structureRecherche d'Informationfr_FR
dc.subjectNLPfr_FR
dc.subjectIRfr_FR
dc.subjectAutomatic text summarizationfr_FR
dc.subjectClusteringfr_FR
dc.titleUsing Clustering and Modified Classification algorithm without a learning corpus for automatic text summarizationfr_FR
dc.typeConference paper
Files
Collections
International Conference Papers