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

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.