Abstract:
In the digital age, summarization is a vital tool for information management,
condensing vast quantities of data into manageable insights. This paper
examines two primary approaches to summarization—computational and
linguistic—each with distinct methodologies and strengths. Computational
techniques, including extractive and abstractive methods, prioritize efficiency
and scalability, employing algorithms and advanced neural networks to generate
summaries. Linguistic approaches, however, treat summarization as an
interpretive process, focused on preserving intent, coherence, and the
communicative goal of the text. This paper compares these two paradigms,
exploring their respective advantages and challenges. Ultimately, we argue that
integrating computational models with linguistic principles offers a more robust
framework for generating human-centered, contextually aware summaries
