Qualitative Analysis of Text Summarization Classification Techniques: A Systematic Approach
Sundas Saif, Department. of Creative Technologies, Air University, Islamabad, Pakistan.
Imran Ihsan, Department. of Creative Technologies, Air University, Islamabad, Pakistan.
Corresponding Author:
Imran Ihsan (iimranihsan@gmail.com)
Abstract:
The rapid increase of textual content on the Internet and in various databases of news stories, scientific reports, legal documents, etc. has increased the significance of automatic text summarization (ATS). An expert manual summary is a time-consuming and nearly impossible task. Automatic summarization classification is essential and beneficial in this regard. Since the 1950s, researchers have been working on ways to improve ATS techniques. This paper proposes a benchmark for a systematic approach to the qualitative analysis of automatic text summarization classification techniques on the research published between 2012 and 2022. The results of a comparison analysis on each benchmark are discussed. The analysis indicates the techniques employed in automatic text summarization classification, as well as the trend toward the most often employed techniques. The paper concludes by discussing potential future directions that could assist researchers in identifying useful future directions to explore.
Keywords:
Automatic Text Summarization; Abstractive; Extractive; Informative; Inductive; Supervised Summarization