Abstract
The process of selecting important information or extracting the same from the original text of large size and present that data in the form of smaller summaries for easy reading is text summarization. Text summarization has now become the need for numerous applications, like market review for analysts, search engine for phones or PCs, business analysis for businesses. The procedure conveyed for outline ranges from structured to linguistic. In this article, we propose a system where we center around the issue to distinguish the most significant piece of the record and produce an intelligent synopsis for them. Proposed system, we don’t require total semantic interpretation for the substance present, rather, we just make a synopsis utilizing a model of point development in the substance shaped from lexical chains. We use NLP, WordNet, Lexical Chains and present a progressed and successful computation to deliver a Summary of the Text.
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Janaki Raman, K., Meenakshi, K. (2021). Automatic Text Summarization of Article (NEWS) Using Lexical Chains and WordNet—A Review. In: Hemanth, D., Vadivu, G., Sangeetha, M., Balas, V. (eds) Artificial Intelligence Techniques for Advanced Computing Applications. Lecture Notes in Networks and Systems, vol 130. Springer, Singapore. https://doi.org/10.1007/978-981-15-5329-5_26
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DOI: https://doi.org/10.1007/978-981-15-5329-5_26
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