Automated Text Summarization and Sentiment Analysis Using Python and Natural Language Processing

Author

Mr. Tulsiram Sharma , Mr. Shubham Agrawal

Abstract

 Unstructured text accounts for over 80% of modern digital data across enterprise systems, presenting substantial computational challenges for efficient information extraction and real-time decision-making. This paper explores the theoretical foundations and technical integration of Automated Text Summarization (ATS) and Sentiment Analysis (SA) utilizing Python and state-of-the-art Natural Language Processing (NLP) methodologies. We evaluate traditional extractive summarization techniques (TF-IDF, TextRank) alongside modern neural abstractive architectures (Seq2Seq, Transformers such as BART and T5). For sentiment analysis, we trace the evolutionary progression from rule-based lexicon systems (VADER, TextBlob) to fine-tuned transformer sequence classifiers. Finally, we present a robust, unified Python pipeline architecture combining automated abstractive summarization with context-aware sentiment classification, evaluating system performance using ROUGE, BERTScore, and classic classification metrics.

Keywords

Natural Language Processing, Text Summarization, Sentiment Analysis, Python Programming, Automated Text Processing

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References

• Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT.
• Lewis, M., et al. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. ACL.
• Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing Order into Text. Empirical Methods in Natural Language Processing (EMNLP).
• Hutto, C.J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. ICWSM.
• Lin, C. Y. (2004). ROUGE: A Package for Automatic Evaluation of Summaries. Text Summarization Branches Out.

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