K-RMS Based Text Summarization Technique
Keywords:
Text summarization, K-means algorithm and K-RMS clustering algorithm.Abstract
Clustering is an unsupervised process of grouping the similar types of data and plays a significant role in the fields of machine learning and data science. K-means is one of such clustering algorithm and it has various application fields. One of these application areas is text mining and many researchers have used K-means for text summarization purpose successfully. In the mean time, it is found that K-means algorithm has some noticeable drawbacks namely low efficiency and more iteration while dealing with the large dataset. To overcome these issues, a modified K-means has been illustrated by Garain et al.[1] called as K-RMS clustering algorithm. In the present work, K-RMS algorithm has been applied for text summarization. The K-RMS algorithm has been tested to OpinosisDataset1.0 dataset and compares the result with K-means clustering algorithm and found the noticeable result.
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https://archive.ics.uci.edu/ml/datasets/Opinosis+Opinion+%26frasl%3B+Review.
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