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In the era of information overload, efficient text summarization is crucial for quick comprehension. This research focuses on abstractive summarization using Spacy and NLTK, comparing their effectiveness on the Kaggle English Article dataset. The model is a natural language processing-based model. It demonstrates superior performance, as evidenced by higher BLEU scores. The study gives insights about the strengths of Spacy’s deep learning architecture and NLTK’s versatility. The research introduces a novel framework, emphasizing data pre-processing, model training, and evaluation using BLEU scores, laying the groundwork for future hybrid approaches.

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SpaCy and NLTK NLP Techniques for Text Summarization: A Comprehensive Comparison

  • 会议论文

  • 发表日期：

    2025年

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  • Raj Dumbre,
  • Pratham Ankalwar,
  • Smita Bhagwat,
  • Dipti Pandit,
  • Mahesh Bhutada,
  • Shirish Gund

摘要

In the era of information overload, efficient text summarization is crucial for quick comprehension. This research focuses on abstractive summarization using Spacy and NLTK, comparing their effectiveness on the Kaggle English Article dataset. The model is a natural language processing-based model. It demonstrates superior performance, as evidenced by higher BLEU scores. The study gives insights about the strengths of Spacy’s deep learning architecture and NLTK’s versatility. The research introduces a novel framework, emphasizing data pre-processing, model training, and evaluation using BLEU scores, laying the groundwork for future hybrid approaches.