Exploring Abstractive Text Summarization: Methods, Dataset, Evaluation, and Emerging Challenges

-The latest advanced models for abstractive summarization, which utilize encoder-decoder frameworks, produce exactly one summary for each source text. This systematic literature review (SLR) comprehensively examines the recent advancements in abstractive text summarization (ATS), a pivotal area in natural language processing (NLP) that aims to generate concise and coherent summaries from extensive text sources. We delve into the evolution of ATS, focusing on key aspects such as encoder-decoder architectures, innovative mechanisms like attention and pointer-generator models, training and optimization methods, datasets, and evaluation metrics. Our review analyzes a wide range of studies, highlighting the transition from traditional sequence-to-sequence models to more advanced approaches like Transformer-based architectures. We explore the integration of mechanisms such as attention, which enhances model interpretability and effectiveness, and pointer-generator networks, which adeptly balance between copying and generating text. The review also addresses the challenges in training these models, including issues related to dataset quality and diversity, particularly in low-resource languages. A critical analysis of evaluation metrics reveals a heavy reliance on ROUGE scores, prompting a discussion on the need for more nuanced evaluation methods that align closely with human judgment. Additionally, we identify and discuss emerging research gaps, such as the need for effective summary length control and the handling of model hallucination, which are crucial for the practical application of ATS. This SLR not only synthesizes current research trends and methodologies in ATS, but also provides insights into future directions, underscoring the importance of continuous innovation in model development, dataset enhancement, and evaluation strategies. Our findings aim to guide researchers and practitioners in navigating the evolving landscape of abstractive text summarization and in identifying areas ripe for future exploration and development.