Current Approaches in Abstractive Text Summarization: A Comprehensive Survey and Analysis

Abstractive text summarization is a complex task in natural language processing aimed at generating concise and coherent summaries that convey the essential meaning of documents. Research has shifted from extractive summarization to abstractive summarization which has new, condensed representations instead of simply extracting and rearranging existing sentences. Using encoder-decoder architecture, where the encoder processes the input text to generate a contextual representation, and the decoder produces the summary based on this representation. This paper provides a review of papers in the field of abstractive summarization from 2018 to 2024. The paper defines a methodology for in-depth analysis of recent research trends, datasets, preprocessing methods, feature selection, methods, evaluation metrics, and challenges with text summarization. The models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) have propelled the field forward.Abstractive summarization finds applications in numerous fields, including news, academic research, legal documents, and social media, enhancing information retrieval and natural language understanding. This paper aims to help researchers know about abstractive summarization methods that are interpretable, robust, and adaptable across various languages and domains.