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An Online Review Summarization on Abstractive Method Using LSTM

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dc.contributor.author Razu, Md.Lutfur Rahman
dc.contributor.author Maulla, Md. Golam
dc.contributor.author Sakib, Sazzad Hossain
dc.date.accessioned 2020-10-22T05:55:22Z
dc.date.available 2020-10-22T05:55:22Z
dc.date.issued 2019-11-26
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/4807
dc.description The revelation of modern technology and advance web application brought a numeric change in online services. This development increases online business faster than before. Now a day’s people are more interested to take the online services. This is because every day millions of people take the services and post the review on internet. But all the reviews are not possible to read out and find the proper answer. Amazon Food fair services is one of them. People are more interested to give their order on internet and they order by watching the reviews of previous customers. For that reasons we are wasting our time to read the reviews. Only a novel summarized review can help us from time wasting. people give their opinion in very different format but our proposed model give a fix length output of the post. en_US
dc.description.abstract In this modern era online services are increased faster than before. This is because online services reviews are very much effected and thousands of review posted every day. This study makes an online review summarization. Two approaches are broadly use in text summarization i). Extractive. ii) Abstractive. In this study we work on abstractive text summarization and used Deep learning method. our proposed method works on Amazon Food fair service customers’ reviews. In this method we execute sequence to sequence model using encoder and decoder architecture. The input and the output length remain same in sequence to sequence model. To solve this problem Encoder and decoder are used where the input and output sequence are of different length. Jointly differing the Recurrent Neural Network(RNNs), Gated Recurrent Neural Network (GRU) or Long Short Term Memory are preeminence as Encoder and Decoder. In this novel study we use LSTM (long short term memory) components. The main difficulties of this paper is training section. this is because most of data are used to train. Finally, this work help user to get a summarized review on Amazon food service. en_US
dc.language.iso en en_US
dc.publisher Daffodil International University en_US
dc.subject Memory en_US
dc.subject Short Term Memory en_US
dc.subject Internet en_US
dc.subject Information Technology en_US
dc.title An Online Review Summarization on Abstractive Method Using LSTM en_US
dc.type Other en_US

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