User Acceptance and Efficiency of NLP-Based News Summarization Systems

Author(s)	Ms. T A Lakshmi, Ms. Honey Johnson
Country	India
Abstract	This study analyses user perceptions and behavioural responses toward NLP-based news summarization systems, focusing on variables such as perceived ease of use, summary quality, user satisfaction, information processing efficiency, and usage intention. Primary data were collected using a structured Likert-scale questionnaire through Google Forms with convenience sampling. The data were analysed using Python, applying techniques such as descriptive statistics, reliability analysis, CSAT, NPS, sentiment analysis, correlation, regression, and inferential tests (t-test, ANOVA, chi-square). The findings indicate a strong positive relationship between summary quality and user satisfaction, and between satisfaction and usage intention. NLP-based summarization significantly improves information processing efficiency by reducing reading time and effort. However, minor limitations in contextual depth were observed. The study provides valuable insights for researchers, developers, and digital platforms to enhance system design, improve user experience, and support effective adoption of AI-driven news summarization tools.
Keywords	NLP, News Summarization, User Acceptance, Satisfaction, Efficiency, Usage Intention
Field	Business Administration
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-11
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.81069

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