Abstract:
In every there are many papers submitted to publishers. In that time, reviewer review that papers
by reading and then give an output like accepted or rejected. Reviewers read every paper one by
one and give a review about that paper is a time consuming. This research only worked for
reviewing the abstraction of a paper. If we review an abstraction by using a model it will be very
effective for saving time. In this research work, we discuss classification algorithms from machine
learning algorithms for finding better result. We make a data set for our model from different
review system. Many kinds of models or algorithms are used for this work. The most important
usable algorithms are modularized Hierarchical Convolutional neural network and another is an
attention-based convolutional neural network. We combine this two algorithms and named as
HCNN for reviewing abstract of papers.
Description:
Work with this project we are study so many paper and journals and work with some algorithms
related to our work. We read many kinds of papers from those papers we describe some work in
this paper those are important for this model. we describe paper about automatic code review,
automatic academic paper review, automatic newspaper review. And the other we read about some
algorithms like convolutional neural networking, attention base neural networking, support vectors
machine.
