Text Summarization of Customer Food Reviews Using Deep Learning Approach

Manore, Kshitija Kiran (2022) Text Summarization of Customer Food Reviews Using Deep Learning Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

The amount of text information has significantly expanded during the past few years. This rise is the result of the quick development of technology. Lengthy and numerous product reviews are attainable, especially in this industry. It is obligatory to have an machine driven program that can condense extensive reviews into concise summaries that include the main thesis. Such automated text summary could seem like a blessing for people. The production of summaries can benefit greatly from abstractive text summarization. It creates non-verbatim phrases while taking the data’s context into account. The production of summaries can benefit greatly from abstractive text summarization. It creates non-verbatim phrases while taking the information’s context into account. In order to increase processing speed by concentrating on a specific section of the review phrase, this article uses Bidirectional Long Short Term Memory (BDLSTM), Long Short Term Memory (LSTM), and Long Short Term Memory with Attention mechanisms. The summary produced using the LSTM model is more accurate than other summaries, according to the results.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	22 Feb 2023 17:30
Last Modified:	02 Mar 2023 09:28
URI:	https://norma.ncirl.ie/id/eprint/6220

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