Transformer-Based Abstractive Text Summarisation for Real-Time Web Applications: A Browser-Integrated System with REST API Architecture - Zvinodashe Revesai; Belinda Ndlovu; Kudakwashe Maguraushe (Journal of Applied Informatics and Computing, 2026)
The exponential growth of digital textual content has intensified the need for efficient, accessible summarisation tools that support information processing across academic, professional, and research domains. While Transformer-based abstractive summarisation models have demonstrated strong performance in benchmark settings, their real-world deployment remains limited due to computational complexity and lack of user accessibility. This study presents a lightweight Transformer-based abstractive text summarisation system, operationalised as a Google Chrome extension and supported by a REST API, enabling seamless integration into everyday user workflows. The proposed system employs an encoder–decoder framework leveraging a pre-trained Transformer-based encoder and a sequence-to-sequence decoder with attention, fine-tuned on the CNN/Daily Mail dataset. Quantitative evaluation on the benchmark dataset achieved ROUGE-1, ROUGE-2, and ROUGE-L scores of 38.21, 16.54, and 35.12, respectively, demonstrating competitive performance relative to established neural baselines. To address the limitations of lexical evaluation metrics, a complementary human evaluation was conducted using a Likert-scale assessment across coherence, informativeness, and fluency, yielding mean scores above 4.0, thereby confirming the qualitative effectiveness of the generated summaries. In addition to model performance, system-level evaluation assessed functional correctness, latency, scalability, and usability within a real-world deployment context. The system demonstrated stable performance under concurrent usage, with an average response time of 4.2 seconds per request and positive user feedback, validating its practical applicability. The findings demonstrate that high-quality abstractive summarisation can be effectively operationalised within a lightweight, browser-integrated architecture, thereby bridging the gap between research-stage neural models and accessible end-user applications. This work contributes to deployment-oriented natural language processing by emphasising usability, modularity, and real-world integration as critical dimensions of system design.