Abstract
This paper provides a comparative analysis of various graph based extraction methods for automatic text summarizations using ROUGE on review dataset. We consider five techniques that include TextRank, LexRank, LSA, Luhn, and Edmundson. These methods concentrate on predicting the semantics of an entity. The experimental results on summarizing the users’ opinions show that the LexRank method gives the best performance among all. Generated summaries are understandable and convey informative opinions.
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© 2016 Springer Nature Singapore Pte Ltd.
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Verma, P., Om, H. (2016). Extraction Based Text Summarization Methods on User’s Review Data: A Comparative Study. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_42
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DOI: https://doi.org/10.1007/978-981-10-3433-6_42
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