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Text Summarization of Customer Reviews Using Natural Language Processing

Atanda, Ridwan (2020) Text Summarization of Customer Reviews Using Natural Language Processing. Masters thesis, Dublin, National College of Ireland.

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Humans have a strong capability to summarize complex and lengthy documents in a simple and concise format. However, in processing and summarizing large volumes of documents within a fraction of seconds, machines outperform the humans. In this particular work, a novel text summarization model was developed by combining extractive and abstractive summarization methods to summarize the large volumes of customer reviews extracted from Amazon data set. The extractive method was used to capture a summary that selects the top-ranking sentences in the corpus using a graph-based TextRank algorithm while these summaries are further fed into a neural network of Long short-term memory (LSTM) to produce the final abstractive summary. The effectiveness of this approach has been measured using the most popularly adopted ROUGE metrics for Natural Language Processing Task. Among multiple models tested, Bi-LSTM is shown to effectively capture the salient information present in the reviews achieving high accuracy and resulted in a concise summary without losing the factual meaning of the reviews.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 18 Jan 2021 16:10
Last Modified: 18 Jan 2021 16:10

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