Hybrid Approach for Automatic Text Summarization for Low-resourced Amharic Language

Automatic text summarization creates a concise version of the given document while retaining the original content's core ideas, logical structure, and understandability. Despite extensive research on summarization in English and other languages, there remains a shortage of work in Amharic due to limited resources and the challenges posed by the language's complex morphology, syntax, and semantics. Moreover, several feature selection methods have been put forth for major languages. Still, there are no published works on how well they work with the Amharic language in a limited resource context. Furthermore, before putting all of the features together, their individual effects on the hybrid summarization have still not been well investigated for the Amharic language. Our research identifies the best features and addresses the linguistic challenges in Amharic summarization by presenting a hybrid strategy that combines extractive and abstractive methodology and data scarcity issues. The extractive approach utilizes statistical and semantic features such as sentence position, length, and semantics to extract essential sentences. Integrating semantic features with the abstractive approach yielded promising results, surpassing even the combination of statistical and semantic features with the abstractive approach.