Abstractive Text Summarization

Abstractive text summarization aims to generate concise, coherent summaries that capture the essence of a longer text, going beyond simply extracting sentences. Current research focuses on improving the accuracy and fluency of these summaries using large language models (LLMs) and sequence-to-sequence models, often incorporating techniques like attention mechanisms, hierarchical structures, and data augmentation to address challenges such as hallucination and factual inconsistency. This field is significant because effective summarization is crucial for efficient information retrieval and comprehension across diverse domains, from news articles to scientific literature and legal documents.