Our Students Participating on The 22nd ISITIA Virtual Conference 2021

Title: Evaluating Extractive Summarization Techniques on News Articles
Authors: Sreeya Reddy Kotrakona Harinatha (batch 2022), Beauty Tatenda Tasara (batch 2022), Nunung Nurul Qomariyah

Abstract—In recent years, due to the rise of deep learning and natural language processing, text summarization has become a huge topic among scholars. Text summarization derives a shorter coherent version of a longer document. There are two methods of summarization namely, abstractive and extractive. This paper focuses on extractive summarization using TextRank and BERT. These algorithms have been tested under various circumstances to determine the best and they all perform better on certain parameters. The goal of this paper is to determine which algorithm performs better as compared to human generated extractive summaries on news dataset. The same dataset was used for both these algorithms and the summaries were evaluated using ROUGE Score. The result showed that TextRank yielded a better ROUGE score as compared to BERT. TextRank showed higher F-measure and recall while BERT had higher precision.

Index Terms—extractive text summarization, Natural Language Processing, BERT, TextRank, supervised machine learning

Presented in the 22nd International Seminar on Intelligent Technology and Its Applications (ISITIA). ISITIA is the annual international conference held by the Department of Electrical Engineering of the Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. The 22nd ISITIA Virtual Conference was held from 21st to 22nd of July, 2021.