Efficient Text Summarization of News Articles Using Natural Language Processing Techniques
Hilda Jerlin C M
, Ajay S , S A Vishal , LOGESH KUMAR K.K , ARUNKUMAR.S
Natural Language Processing (NLP), Text Summarization, Facebook's BART, Microsoft SpeechT5, ROUGE, Graphical User Interface (GUI)
This paper aims to develop an efficient news summarization tool that utilizes advanced natural language processing (NLP) techniques, specifically Facebook's BART and Microsoft SpeechT5 models, to automatically generate informative and concise summaries of news articles. The tool will be equipped with a graphical user interface (GUI) that allows users to input a query or choose a category, and display the summarized news articles. This Project builds with a large dataset of news articles from diverse sources and preprocessed them to make them suitable for analysis. The BART and SpeechT5 models were fine-tuned on the dataset, and their performance was evaluated using metrics such as ROUGE. The system's accuracy, efficiency, and ease of use make it an ideal solution for anyone who wants to stay up-to-date on the latest news.
"Efficient Text Summarization of News Articles Using Natural Language Processing Techniques", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.318-321, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001050.pdf
Volume 11
Issue 2,
February-2024
Pages : 318-321
Paper Reg. ID: TIJER_151067
Published Paper Id: TIJERB001050
Downloads: 000281
Research Area: Science and Technology
Country: THIRUVALLUR, TAMILNADU, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
