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Survey of Text Document Summarization Based on Ensemble Topic Vector Clustering Model

Publication Type : Book Chapter

Publisher : Springer Nature

Source : In: Joby, P.P., Balas, V.E., Palanisamy, R. (eds) IoT Based Control Networks and Intelligent Systems. Lecture Notes in Networks and Systems, vol 528. Springer,Singapore. https://doi.org/10.1007/978-981-19-5845-8_60

Url : https://link.springer.com/chapter/10.1007/978-981-19-5845-8_60

Campus : Chennai

School : School of Computing

Year : 2023

Abstract : Text document analysis has recently emerged as a promising strategy in content summarization domain. The analysis can be carried out in two stages: Text abstraction, the process of summarizing a document by including the most critical information from the original document; Text summarization, the process of generalizing redundant information in order to determine the significance of the issue. Automated text summarization is a technique used for extracting the most meaningful information from a document or group of related papers and assembling it into a concise version by retaining the overall meaning of the text document. The text abstraction model is mostly associated with the content extraction process, whereas to perform text summarization, the Natural Language Processing (NLP) technique is used to extract the necessary information from a lengthy text document. To perform efficient and automated text processing and summarization, this research study suggests a novel ensemble topic vector clustering technique, which utilizes Semantic Analysis (SA) to analyze the content. Further, the proposed study concentrates on the process of topic summarizaton to investigate various strategies and perform problem identification. Finally, the proposed study examines the significance of summarization implementation by comparing it with similar existing approaches.

Cite this Research Publication : Bharathi Mohan, G., Prasanna Kumar, R. (2023), "Survey of Text Document Summarization Based on Ensemble Topic Vector Clustering Model," In: Joby, P.P., Balas, V.E., Palanisamy, R. (eds) IoT Based Control Networks and Intelligent Systems. Lecture Notes in Networks and Systems, vol 528. Springer,Singapore. https://doi.org/10.1007/978-981-19-5845-8_60

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