Advanced Curriculum-Based Abstractive Text Summarization with T5 Language Model and Reinforcement Learning

The research proposes a new method of abstractive summarization through hybridizing curriculum reinforcement learning (RL) with the T5 pre-trained language model. The approach differs from traditional methods by learning to accumulate summarization capabilities through a task progressive curriculum. Our system begins by summarizing single sentences before developing its capabilities to create summaries of multi paragraph documents. Using curriculum learning enables the model to produce both superior prescreen summaries and more generalized results. A reinforcement learning process maximizes summary outputs based on factual accuracy together with coherence and relevance by using a reward function. Our research methodology reaches state-of-the-art performance on abstractive summarization thereby establishing a new benchmark for the field.