Abstract
Automatic text summarization (ATS) is a widely used approach. Through the years, various techniques have been implemented to produce the summary. An extractive summary is a traditional mechanism for information extraction, where important sentences are selected which refers to the basic concepts of the article. In this paper, extractive summarization has been considered as a classification problem. Machine learning techniques have been implemented for classification problems in various domains. To solve the summarization problem in this paper, machine learning is taken into consideration, and KNN, random forest, support vector machine, multilayer perceptron, decision tree and logistic regression algorithm have been implemented on Newsroom dataset.
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Pattanaik, A., Mishra, S.S., Das, M. (2020). A Comparative Study of Classifiers for Extractive Text Summarization. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_16
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DOI: https://doi.org/10.1007/978-981-15-1884-3_16
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