Automatic Summarization of Scientific Articles

Abstract

The process of scientific research starts with studying the state of the art, and this means an infinite number of publications. Hence, Automatic Summarization of Scientific Articles will help scholars, researchers, or anyone interested in a specific topic by summarizing and shortening the articles they have to read, this will save them a lot of time allowing them to read more articles and gather more information. In conclusion, the proposed solution is divided into two approaches the Extractive approach and the Abstractive approach. For the Extractive approach, different embedding techniques and different approaches were used. But, the one that gave the best results was using GloVe as an embedding technique, and as for the number of sentences to be extracted after many experiments a new approach was applied and it gave a promising result with 0.1244 Rouge-2 f-measures. And as for the Abstractive approach, different pre-trained models were used to solve this problem, but the one that gave the highest results was the GPT-2 XL pre-trained model with 0.38733 Rouge-2 f-measure. As for the dataset used was the CL-SciSumm 2019.