Question Summation and Sentence Similarity using BERT for Key Information Extraction

Abstract

Abstractt: In this study, we explore the use of BERT-based techniques for summarization and sentence similarity check in the context of important question answering. We propose a novel approach that combines BERT-based summarization and semantic similarity checking to extract key information from textual inputs and predict the most important Questions. Our experiments demonstrate that our approach achieves state-of-the-art performance on several benchmark datasets, surpassing traditional machine learning and deep learning techniques. We also evaluate the effectiveness of our approach on real-world examples and show that it can be applied to a wide range of important question answering tasks, including medical diagnosis, legal case analysis, and financial forecasting. Our results suggest that BERT-based summarization and sentence similarity check can greatly improve the accuracy and efficiency of important question answering systems, and have the potential to benefit a variety of domains and applications. This abstract provides a brief overview of the main goals, methods, and results of the study, highlighting the key contributions and potential implications of the proposed approach. It also mentions some of the domains and applications that could benefit from the use of BERT-based techniques for summarization and sentence similarity check in important question answering