6 min readApr 5, 2025
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Text summarization is a cornerstone of natural language processing (NLP), enabling us to distill lengthy documents into concise summaries. Two popular extractive methods — TF-IDF (Term Frequency-Inverse Document Frequency) and TextRank — offer distinct approaches to this task. In this article, we’ll explore these techniques using a Python implementation, break down their background processes, and explain the mathematical underpinnings, including the Markov state model in TextRank and sentence-level TF-IDF scoring. The full code is provided and integrated into the discussion for clarity.
1. Overview of the Approach
Our Python implementation uses libraries like nltk for text preprocessing, sklearn for TF-IDF vectorization, numpy for matrix op…
6 min readApr 5, 2025
–
Text summarization is a cornerstone of natural language processing (NLP), enabling us to distill lengthy documents into concise summaries. Two popular extractive methods — TF-IDF (Term Frequency-Inverse Document Frequency) and TextRank — offer distinct approaches to this task. In this article, we’ll explore these techniques using a Python implementation, break down their background processes, and explain the mathematical underpinnings, including the Markov state model in TextRank and sentence-level TF-IDF scoring. The full code is provided and integrated into the discussion for clarity.
1. Overview of the Approach
Our Python implementation uses libraries like nltk for text preprocessing, sklearn for TF-IDF vectorization, numpy for matrix operations, and networkx for graph-based ranking. The sample text we’ll summarize is:
Machine learning is a powerful tool for data analysis. It allows computers to learn from data and improve over time. Learning algorithms are at the core of this technology. These algorithms can identify patterns and make predictions. Powerful algorithms drive advancements in artificial intelligence. AI systems are transforming industries worldwide.