End To End NLP Implementation with Deployment Github Action - Text Summarization

The exponential growth of textual data, automatic text summarization has become an essential tool for extracting key information. It presents an end-to-end implementation of an abstractive text summarization model based on transformer architectures. The proposed system leverages a pre-trained language model, such as BART or T5 to generate coherent and concise summaries. The deployment is automated using GitHub Actions, ensuring a seamless CI/CD pipeline for real-world applications. Experimental results demonstrate the effectiveness of the model in generating high-quality summaries with improved ROUGE scores compared to baseline methods. This implementation provides a robust and automated solution for text summarization, making it adaptable for various NLP applications in academia and industry. Hence, automatic text summarization has become a desirable tool in today’s information age. It produces concise, fluent and readable summaries from larger bodies of text. Keywords: NLP, Abstractive Summarization, Transformers, Docker, GitHub-Actions, Cloud Deployment