Abstract
In recent times, we have abundant sources for information out of which not every detail is significant. So the point of concern is identifying the most relevant and important information out of vast data. The focus should be given to the relevant information present in the entire data in such a way that we can understand the entire gist of the text. Considering the above concern as our problem statement, in this paper we discuss “Text Summarization”—it is the process of generating pertinent and significant text from the entire information. Text resources include websites, text documents, and direct text feed from users. Text Summarization can be performed using 2 methods—Extractive Summarization and Abstractive summarization. Here we are going to generate a summary from various types of text sources using extractive techniques. In extractive techniques, we have used a frequency driven approach where the relevance of a particular sentence is measured through sentence weightage and term frequency. We measured the accuracy of the model using a sequence matcher which provides us the relevance between generated summary and human perceived summary.
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Mohan Kalyan, V., Santhaiah, C., Naga Sri Nikhil, M., Jithendra, J., Deepthi, Y., Krishna Rao, N.V. (2021). Extractive Summarization Using Frequency Driven Approach. In: Mai, C.K., Reddy, A.B., Raju, K.S. (eds) Machine Learning Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4046-6_18
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DOI: https://doi.org/10.1007/978-981-33-4046-6_18
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