Text-Summarization-of-Breaking-News-Using-Fine-tuning-BART-Model.pptx

Text summarization of braking news using fine tuning BART model

1. Text Summarization of Breaking News Using Fine-tuning BART Model This section provides an overview of the process of text summarization using the Fine-tuning BART model. ZALIHA MUSTAPHA KARAYE MSC22CS020

2. Introduction 1 Automatic text summ arization summarization is the process of shortening a set of d ata computationally to form a summary that preserves the most important information of the original content.it is an active field or a research area in natural pr ocessing language NLP. While NLP is an application area of machine learning and ML is one of the m ajor areas of Artificial intelligence (AI). 2 Extractive text s ummarization in this approach, sentences and paragraphs are extra cted from source document and concatenated to form a summa ry. 3 Abstractive text summarization abstractive text summarization approach, advanced NLP techniques are used to understand the source text and generate a shorter text that conveys the most important information of the original text.

3. Problem Statement Information Overload The rapid generation of news articles has led to information overload, making it challenging for readers to stay updated. Need for Efficiency Readers often lack the time to go through lengthy news articles, necessitating the need for efficient summarization. Accuracy and Conciseness The challenge lies in creating concise yet accurate summaries that capture the essence of the news.

4. Background of the Study 1 Text Summarization Basics Understanding the fundamentals of text summarization is crucial for applying advanced models like BART. 2 Language Understanding An in-depth understanding of natural language processing forms the backbone of developing effective summarization techniques. 3 Previous Research Reviewing past studies and techniques provides valuable insights for enhancing the summarization process.

5. Text Summarization Techniques Extractive Summarization Extractive techniques involve identifying and extracting important sentences or paragraphs from the original text. Abstractive Summarization Abstractive methods focus on generating new phrases to capture the essence of the content, often requiring advanced language models.

6. RESEARCH AIM AND OBJECTIVES RESEARCH AIM The aim of this study is to develo ped an advanced, efficient model for the real-time summarization RESEARCH OBJECTIVES 1. To improve the accuracy of breaking news summarization by fine - tuning Bart m odel. 2.To implement a mechanism within the model to validate and verify the factual content of news, ensuring the accuracy and reliability of the summaries. 3. To evaluate the model effectiveness in different breaking news scenarios to test its reliabilit y under real world conditions .

7. RESEARCH QUESTIONS 1. To improve the accuracy of breaking news summarization by fine - tuning Bart m odel. 2.To implement a mechanism within the model to validate and verify the factual content of news, ensuring the accuracy and reliability of the summaries. 3. To evaluate the model effectiveness in different breaking news scenarios to test its reliabilit y under real world conditions .

8. Fine-tuning BART Model Data Collection Acquiring a diverse and relevant dataset is the initial step in preparing the BART model for fine-tuning. Model Training The BART model is trained on the collected dataset to adjust its parameters for text summarization tasks. Validation and Optimization Validating model performance and optimizing fine-tuning parameters are essential for achieving high-quality summaries.

9. Evaluation Metrics Precision Precision The precision score measures the exactness of the summaries generated by the BART model. Recall Recall Recall evaluates the capability of the model to capture all the important information from the original text. F1 Score F1 Score The F1 score balances the trade-off between precision and recall to provide a comprehensive evaluation.

10. Experimental Setup Data Source News Articles from Various Outlets Hardware High-performance GPUs for Model Training Software PyTorch for Implementing BART Model and Evaluation

11. Results and Analysis 1 Summary Quality Analyzing the quality of the generated summaries based on human evaluations is integral to assessing the results. 2 Performance Comparison Comparing the BART model's summaries with human-generated versions provides valuable insights into its effectiveness.

12. Conclusion Advancements in Summarization The study showcases the potential of fine-tuning the BART model for improved text summarization in the context of breaking news. Future Implications Implementing advanced models like BART can significantly enhance the speed and accuracy of news report summarization.