Mastering Extractive Summarization: A Theoretical and Practical Guide to TF-IDF and TextRank
Last Updated on December 9, 2025 by Editorial Team
Author(s): VARUN MISHRA
Originally published on Towards AI.
Mastering Extractive Summarization: A Theoretical and Practical Guide to TF-IDF and TextRank
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.
This article discusses two significant techniques for extractive summarization in natural language processing: TF-IDF and TextRank. It delves into their theoretical foundations and practical implementations using Python, detailing the preprocessing, scoring, and ranking processes involved in each method. The author elucidates the importance of each technique, exploring how TF-IDF computes term significance while TextRank utilizes a graph-based ranking approach to assess sentence relevance, ultimately providing code examples to illustrate their applications and value in creating concise document summaries.
Read the full blog for free on Medium.
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