EXTRACTIVE TEXT SUMMARIZATION

Abstract

Over the past decade, there has been remarkable growth in the domains of Artificial Intelligence (AI), Machine Learning (ML), and Data Science, presenting vast opportunities in diverse industries such as healthcare, finance, and transportation. Notably, the field of Natural Language Processing (NLP), a branch of AI and ML, has experienced significant advancements. NLP involves the machine-based processing and understanding of human language. Among its various applications, text summarization holds prominence as it enables machines to condense lengthy texts into concise summaries. This project highlights the utilization of multiple extractive text summarization techniques, including BERT, GPT-2, KL summarizer, Luhn, LEX, and Word Rank. The resultant extractive summaries are then evaluated against human-generated summaries using three distinct scoring methods: Rouge Score, BERT Score, and Mover Score. Through this project, we demonstrate the efficacy of these techniques in generating summaries and assess their quality by comparing them against summaries produced by humans using the specified scoring metrics.