Text Summarization Based Named Entity Recognition for Certain Application Using BERT

• Published in: 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) pp. 1136 - 1141
• Main Author: Tummala, Indira Priyadarshini
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.08.2024
• Subjects: Accuracy; BERT; Classification algorithms; Deep learning; Named entity recognition; Natural Language Processing; Text processing; Text recognition; Text summarization; Training; Transformers; Tuning
• DOI: 10.1109/ICoICI62503.2024.10696673

Text summarization is gaining attention from readers who want to understand the main concept of the entire text. One of the natural language processing forms, "Named Entity Recognition" (NER) is used to summarize the text with deep learning. Semantic and syntactic relationships between the words are well understood by NER, and it uses data preprocessing, which mainly detects different entities from the text and classifies them into various categories. In this work, text reading, text processing, text formulation, and text evaluation are performed in the summarization of the text using BERT, Transformers algorithms. Deep learning concepts are incorporated to reduce the training loss and increase validation accuracy with fine tuning of BERT algorithm. NER is implemented on Spacy and NLTK frameworks, and the code was executed on Colab/Python)