Automating News Processing: a Machine Learning Framework for Efficient Classification and Summarization

The escalation of digital news content makes it necessary to develop automated systems that facilitate the organization and shortening of information effectively. Traditional Natural Language Processing (NLP) strategies frequently run into difficulties in scalability and potency, particularly with regard to text classification and the art of summarization. This research introduced a News Classification and Summarization Model using BBC News as its dataset, concentrated on extractive summarization for producing succinct and accurate summaries. The process began with pre-processing activities, including data cleaning and transformation, followed by feature extraction. Several machine learning algorithms are assessed to determine the optimal model for classifying news into predefined categories. Random Forest emerged as the best model, accomplishing an outstanding accuracy rate of 97%. On the other hand, an extractive approach of summarization was used through tokenization, NLP methods, scoring systems to obtain the most important details from the content performing summarization without losing core meaning. This strategy applied in the model also proved efficient when dealing with normal textual data other than news article since it enables categorizing of the data as well as producing summaries. In conclusion, the integrated framework showed promise for application in different disciplines to enhance the speed and efficiency of information retrieval and analysis by eliminating the need for manual classification and summary.