Text Summarization Using Dual Encoder Architecture

In the modern digital era, textual data plays a pivotal role in the dissemination of information through various forms such as news articles, reports, and papers. With the exponential growth in the production of such content, there arises an urgent need to efficiently extract meaningful and insightful knowledge from this vast pool of text. natural language processing (NLP) has been developed to tackle this challenge, enabling machines to comprehend textual data and derive valuable information. Text summarization, a critical NLP task, involves condensing large volumes of text into shorter, more digestible summaries while retaining key factual data. This process aids in managing the overwhelming influx of information and makes it more accessible and actionable. In this paper, the research aims to implement a text summarization system using the BART (Bidirectional and Auto-Regressive Transformers) model, augmented with various ranking-based algorithms, to enhance the quality and relevance of the summaries. This approach seeks to introduce a human-like touch to the summarization process, ensuring that the generated summaries are not only concise but also contextually accurate and meaningful for the end-user.