A review on optimization-based automatic text summarization approach

The significance of automatic text summarization (ATS) lies in its task of distilling textual information into a condensed yet meaningful structure that preserves the core message of the original content. This summary generated by ATS plays a crucial role in simplifying the processing of textual information, as it captures the primary ideas of the source text while eliminating lengthy and irrelevant textual components. At present, the landscape of ATS is enriched with a multitude of innovative approaches, with a notable focus on optimization-based methods. These optimization-driven ATS techniques have introduced new perspectives, illuminating the field with their heightened accuracy in terms of metrics like ROUGE scores. Notably, their performance closely rivals other cutting-edge approaches, including various methodologies within the realm of machine learning and deep learning. The review presented in this paper delves into recent advancements in extractive ATS, centering mainly on the optimization-based approach. Through this exploration, the paper underscores the gains and trade-offs associated with adopting optimization-based ATS compared to other strategies, specifically with the application of real-time ATS. This review serves as a compass, pointing towards potential future directions that the optimization-based ATS approaches should consider traversing to enhance the field further.

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