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.
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.
