Text summarization entails the task of generating a concise overview of a relatively lengthy textual material, capturing the core substance or essence of its comprehensive content. This summary is expected to retain the fundamental meaning intended by the original text, while upholding cohesiveness and fluency. In the realm of automatic text summarization (ATS), two principal methodologies exist: Extractive and Abstractive summarization. Extractive summarization has seen significant exploration, especially within languages deemed resource-rich. As this avenue matures within these languages, scholarly attention has turned toward the Abstractive approach to summarization. However, this progression might not necessarily extend to languages with low resources. Within this study, we present a review of automatic text summarization literature, encompassing both abstractive and extractive approaches, with a specific focus on languages characterized by low resource availability. Our review concentrates on papers disseminated within conferences and journals spanning the timeframe from 2018 to 2023. The review meticulously elaborates on the methodologies, techniques, and procedures employed, as well as the aspects of data acquisition and preprocessing, alongside the evaluation strategies utilized. Executed through the systematic literature review (SLR) methodology, this review serves the purpose of offering a roadmap or guideline for researchers interested in delving into text summarization within a low-resource setting.