Hybrid Zero-Shot NLP Pipeline for Text Summarization and Question Generation

Inioluwa Daniel Osibajo, Oluwaseyi Ezekiel Olorunshola, Fatimah Adamu-Fika and Tsentob Joy Samson
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Inioluwa Daniel Osibajo: Department of Computer Science, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.
Oluwaseyi Ezekiel Olorunshola: Department of Computer Science, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.
Fatimah Adamu-Fika: Department of Cyber Security, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.
Tsentob Joy Samson: Department of Computer Science, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 7, 342-354

Abstract: This study presents a sophisticated hybrid zero-shot Natural Language Processing (NLP) pipeline for text summarization and multiple-choice question (MCQ) generation, specifically designed for low-resource educational environments. The system integrates Bidirectional Encoder Representations from Transformers (BERT) for extractive summarization, Bidirectional and Auto-Regressive Transformers (BART) for abstractive summarization, and the Text-to-Text Transfer Transformer (T5) for MCQ generation. Built using the Hugging Face Transformers library, Natural Language Toolkit (NLTK), Spa Cy, and Sentence Transformers, the pipeline operates efficiently on a 12 GB Graphics Processing Unit (GPU) without the need for model fine-tuning. The workflow involves preprocessing academic texts, identifying key sentences through BERT and TextRank—a graph-based ranking algorithm—generating coherent and concise summaries with BART, and producing diverse, contextually relevant MCQs using T5. Evaluations were conducted on user-generated academic texts and the CNN/Daily Mail dataset for benchmarking. The system achieved a BERT Score F1 of 0.87, Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1 and ROUGE-L of 0.54, Bilingual Evaluation Understudy (BLEU) of 0.20, Metric for Evaluation of Translation with Explicit OR dering (METEOR) of 0.35, a compression ratio of 0.37, coherence score of 0.50, and 80% human-rated MCQ relevance—outperforming Generative Pre-trained Transformer (GPT-3) baselines. To assess educational impact, a study was conducted with 20 students of average academic standing using a 25-mark test generated by the pipeline. Results showed that 13 students scored above 20, 4 scored between 15–20, and 3 scored between 10–15, indicating that 85% of participants exceeded a 60% proficiency threshold. Qualitative analysis revealed minor factual inaccuracies in 10% of summaries and relevance drift in 15% of MCQs, highlighting areas for further enhancement. Overall, the study demonstrates the practical potential of transformer-based hybrid NLP pipelines for scalable, accessible educational content creation in resource-constrained contexts.

Date: 2025
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