Abstract
Document classification is one among the major NLP tasks that facilitate mining of text data and retrieval of relevant information. Most of the existing works use pre-computed features for building the classification model. Large-scale document classification relies on the efficiency or appropriateness of feature selection for document representation. The proposed system uses text summarization for automated feature selection to build the classification model. This work considers feature selection as a sentence extraction task which can be done using extractive text summarization. The method will have the advantage of reduced feature space, as classifier will be trained on shorter summary than the original document. Also, deep learning-based summarization generates the most relevant features resulting in improved efficiency and accuracy of the classifier. Experiments showed that classification based on features generated using deep learning provides better classification accuracy.
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Assainar Hafnan, P.P., Mohan, A. (2018). Summary-Based Document Classification. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_16
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DOI: https://doi.org/10.1007/978-981-10-8633-5_16
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