Text Summarization Techniques Using Natural Language Processing: A Systematic Literature Review
In recent years, data has been growing rapidly in almost every domain. Due to this excessiveness of data, there is a need for an automatic text summarizer that summarizes long and numerical data especially textual data without losing its content. Text summarization has been under research for decades and researchers used different summarization methods by using natural language processing and combining various algorithms. This paper presents a systematic literature review by showing a survey of text summarization methods and explains the accuracy of these methods used for text summarization. The paper first introduced some concepts of extractive and abstractive text summarization and also define how deep learning models can be used for the improvement of text summarization. This paper aims to identify the current utilization of text summarization in different application domains. Different methodologies are discussed for text summarization. To carry out this SLR, twenty-four published articles have been chosen carefully for this domain. Moreover, it discusses issues and challenges which are investigated in different application domains using text summarization methods. Lastly, the existing work of different researchers has been carried out for further discussion.
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