Text Summarization Based Named Entity Recognition for Certain Application Using BERT

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...

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Bibliographic Details
Published in2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) pp. 1136 - 1141
Main Author Tummala, Indira Priyadarshini
Format Conference Proceeding
LanguageEnglish
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
Online AccessGet full text
DOI10.1109/ICoICI62503.2024.10696673

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Summary: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)
DOI:10.1109/ICoICI62503.2024.10696673