4 February 2013 Using clustering and a modified classification algorithm for automatic text summarization
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In 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 we have compared our results to another summarization system called UNIS.1 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.
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Abdelkrime Aries, Abdelkrime Aries, Houda Oufaida, Houda Oufaida, Omar Nouali, Omar Nouali, } "Using clustering and a modified classification algorithm for automatic text summarization", Proc. SPIE 8658, Document Recognition and Retrieval XX, 865811 (4 February 2013); doi: 10.1117/12.2004001; https://doi.org/10.1117/12.2004001


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