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Summarizing Research Papers with Transformer Models
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DOI：10.1007/978-3-032-07244-3_21.png)
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Abstract
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The exponential growth of academic publications has created an urgent need for reliable and automated summarization tools that can help researchers quickly grasp the essence of scholarly documents. This study investigates the effectiveness of two transformer-based models-T5-small and BART-large-for summarizing full-length research papers. These models were selected for their complementary design: T5's unified text-to-text framework enables general-purpose summarization, while BART's bidirectional encoder-decoder architecture offers rich contextual modeling. Using a preprocessed dataset of over 50,000 research abstracts from Kaggle's arXiv collection, both models were fine-tuned and evaluated on 15 representative samples using standard NLP metrics, including BLEU, ROUGE-1/2/L, and BERTScore. Experimental results show that BART outperforms T5 across most metrics, achieving ROUGE-1 (45.67%), ROUGE-2 (40.35%), ROUGE-L (41.54%), BLEU (8.51), and BERTScore F1 (89.68%), while T5 achieved a slightly lower BERTScore F1 (88.62%) but showed better performance in certain semantic aspects. The average word length was also assessed to ensure lexical consistency. A focused case study on the complex NLP paper Attention Is All You Need revealed performance limitations, with ROUGE-1 dropping to 25.62% and BERTScore F1 to 80.36%, indicating challenges in handling dense, technical content with mathematical expressions and domain-specific language. This work provides a practical, comparative benchmark for transformer-based academic summarization and highlights the need for future research in domain-specific fine-tuning, hybrid summarization architectures, and human-in-the-loop evaluation frameworks to further enhance summary quality and robustness.
Keywords:
Summarization
Transformer
Research Papers
T5
BART
BLEU
ROUGE
BERT Score
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