Abstract
Text summarization method produces the shorter or abstract version of text after giving the large source text. It provides the meaningful information of the source text, i.e. the text’s meaning is intact and accurate. Text summarization tools have a powerful impact on today’s world due to the increasing information with a massive rate on the Internet. It is very difficult for a person to describe and ingest the whole content. The manual conversion or summarization is very difficult task, hence automation is need. The automation can be achieve using artificial intelligence techniques. Text summarization methods are classified into two categories: Extractive and abstractive. The extractive method, as its name suggests, consists of extracting important sentences or paragraph from some source of text and rejoining them to get the summarized form of the source content. The criteria for evaluating an importance of a sentence or paragraph is based on the statistical features parameter of the sentences, and the abstractive method is all about knowing the source text and re-writing the text in a few words that describes the whole source text. In addition, this method uses a linguistic approach to check and interpret the source text. In this article, extractive text summarization methods are applied to the job. The validation of the model is performed using the bench-marked source text. From the obtained result, it is evident that the summarization model performs well and do the summarization which is very precise and meaningful.
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Soni, V., Kumar, L., Singh, A.K., Kumar, M. (2020). Text Summarization: An Extractive Approach. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_57
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