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A Comparative Study of Text Summarization on E-mail Data Using Unsupervised Learning Approaches
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In: Dissertations (2020)
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Abstract:
Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study in text processing, especially for the task of summarization. This research employs a quantitative and inductive methodologies to implement the Unsupervised learning models that addresses summarization task problem, to efficiently generate more precise summaries and to determine which approach of implementing Unsupervised clustering models outperform the best. The precision score from ROUGE-N metrics is used as the evaluation metrics in this research. This research evaluates the performance in terms of the precision score of four different approaches of text summarization by using various combinations of feature embedding technique like Word2Vec /BERT model and hybrid/conventional clustering algorithms. The results reveals that both the approaches of using Word2Vec and BERT feature embedding along with hybrid PHA-ClusteringGain k-Means algorithm achieved increase in the precision when compared with the conventional k-means clustering model. Among those hybrid approaches performed, the one using Word2Vec as feature embedding method attained 55.73% as maximum precision value.
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Keyword:
Computer Engineering; Computer Sciences; Electronic mail; K-means clustering; PHA-Clustering Gain; ROUGE II; Text Summarization; Unsupervised Learning
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URL: https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1220&context=scschcomdis
https://arrow.tudublin.ie/scschcomdis/205
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Detecção de spam em mensagens SMS utilizando aprendizagem de máquina ; Spam detection in sms messages using machine learning
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Tibola, Rafael Henrique. - : Universidade Tecnológica Federal do Paraná, 2018. : Medianeira, 2018. : Brasil, 2018. : Ciência da Computação, 2018. : UTFPR, 2018
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A case study of Japanese students: e-mail exchange in English: feedback focusing on communicability
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The Role, Status and Style of Workplace Email: a Study of Two New Zealand Workplaces
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Code-switching behavior in email writing among fluent bilinguals of Bulgarian and English
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In: Graduate Student Theses, Dissertations, & Professional Papers (2002)
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