Abstract Classification

Abstract

As a result of the accumulation of unindexed research papers and public articles in libraries and research centers, a classification method is required to classify these research papers into their proper class. Abstract classification takes an abstract as an input and outputs a classification (e.g., physics, math) that is the class to which the given paper belongs. This will be done using AI and machine learning to teach the model how to classify papers through their abstracts. manually reading through documents is notoriously difficult for humans, as they have to go through all of these abstracts one by one to be able to give a decision for the suitable classes for each abstract, this process will take an unreasonable amount of time to have the results, regardless of the accuracy the human can give. Many papers need to be categorized and arranged in libraries and other study facilities. A new approach must be created for classifying and organizing these articles that is both accurate and time-efficient. This project is able to group papers into “categories” based on the relevant topics using a machine learning model that can classify documents, the results are presented to the user using a web application. In conclusion, software that classifies abstracts will offer a simple and painless approach to indexing and arranging a collection of research publications.