Revolutionizing Text Summarization: A Breakthrough in Content Compression

doi:10.23940/ijpe.24.01.p6.4047

Abstract

Abstract: In the current digital epoch, the vast expanse of information has revolutionized the accessibility of knowledge and perspectives. Nevertheless, this information abundance has introduced challenges in navigating and comprehending the deluge of textual data. The surge in online news articles, research papers, reports, and diverse document genres has accentuated the necessity for proficient document summarization techniques. Traditional manual methods of summarization are time-intensive and influenced by subjective biases. In contrast, the synergy between Natural Language Processing (NLP) and machine learning has unlocked the potential for automated document summarization, promising efficient information consumption and informed decision-making. This research paper delves into the convergence of these factors. It is driven by the Longformer model's distinctive capability to manage extensive texts while retaining contextual coherence—a potential solution to the hurdle of large document summarization. By capitalizing on the Longformer's architecture, this study endeavors to exploit its prowess in generating cohesive summaries from lengthy source documents, thereby amplifying the accessibility of intricate information.

Key words: CNN/DailyMail corpus, extensive document summarization, longformer paradigm, natural language processing, transformer-based paradigms

Nidhi Mishra, Farhan Khan, and Amit Mishra. Revolutionizing Text Summarization: A Breakthrough in Content Compression [J]. Int J Performability Eng, 2024, 20(1): 40-47.

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