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Text summarization plays an important role in learning enhancement by extracting valuable insights from the original text. From an educational point of view, an effective dialogue summary can help students, teachers, and researchers structure the content better by getting precise and concise content. This research investigates the use of a fine-tuned T5-base model for creating abstractive summaries of educational dialogues, utilizing the SAMSum dataset, which consists of dialogue-based interactions accompanied by human-crafted summaries. This research adds to educational technologies by introducing a summarization model that enhances reading comprehension in academic settings, particularly for learners with extensive digital materials or those facing learning difficulties. We assessed the model's efficiency through the ROUGE metric, obtaining ROUGE-1: 0.50, ROUGE-2: 0.25, and ROUGE-L: 0.41, showcasing competitive results relative to current state-of-the-art models. The experimental findings showed that our model performed better.

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Enhancing Learning Through Summarization Using a Fine-Tuned T5 Model

  • 会议论文

  • 发表日期：

    2026年

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  • Muhammad Ehsan,
  • Rung-Ching Chen,
  • Su-Wen Huang

摘要

Text summarization plays an important role in learning enhancement by extracting valuable insights from the original text. From an educational point of view, an effective dialogue summary can help students, teachers, and researchers structure the content better by getting precise and concise content. This research investigates the use of a fine-tuned T5-base model for creating abstractive summaries of educational dialogues, utilizing the SAMSum dataset, which consists of dialogue-based interactions accompanied by human-crafted summaries. This research adds to educational technologies by introducing a summarization model that enhances reading comprehension in academic settings, particularly for learners with extensive digital materials or those facing learning difficulties. We assessed the model's efficiency through the ROUGE metric, obtaining ROUGE-1: 0.50, ROUGE-2: 0.25, and ROUGE-L: 0.41, showcasing competitive results relative to current state-of-the-art models. The experimental findings showed that our model performed better.