Keyword Extraction and Text Summarization Techniques

Two of the most practically useful text analysis tasks are keyword extraction — identifying the most important terms in a document — and text summarization — condensing a long document into a shorter version that preserves key information. Both tasks address the fundamental challenge of information overload: as the volume of text requiring attention exceeds available reading time, automated tools for identifying what matters become genuinely valuable. This guide covers the main approaches to each task, their relative strengths and limitations, and how to choose the right technique for your application.

Keyword Extraction: TF-IDF and Beyond

TF-IDF (Term Frequency-Inverse Document Frequency) is the foundational keyword extraction technique. A word's TF-IDF score is high when it appears frequently in the target document but rarely across the broader document collection — indicating that it is both topically relevant and discriminative. For single-document keyword extraction without a reference corpus, variants like TF-IDF with stop word removal and part-of-speech filtering (keeping only nouns and noun phrases) work well. RAKE (Rapid Automatic Keyword Extraction) identifies keyword candidates based on word frequency and co-occurrence within phrases, without requiring a reference corpus. YAKE (Yet Another Keyword Extractor) uses statistical features from a single document — word frequency, position in text, term co-occurrence — to score candidate keywords without any machine learning. These unsupervised methods require no training data and work across domains.

Graph-Based Methods: TextRank for Keywords and Sentences

TextRank, inspired by Google's PageRank algorithm, builds a graph where nodes represent words (or sentences for summarization) and edges represent co-occurrence or similarity relationships. Nodes with many connections to other high-scoring nodes receive high scores. For keyword extraction, words that co-occur with many other frequently co-occurring words are identified as keywords. For summarization, sentences that are most similar to many other sentences in the document are identified as the most central and selected for inclusion in the summary. TextRank produces extractive summaries — actual sentences from the original document — which are always grammatically correct and factually grounded, unlike abstractive methods that generate new text. The Python summa library provides a one-line TextRank implementation for both keywords and summarization.

Extractive vs. Abstractive Summarization

Extractive summarization selects and concatenates the most important sentences from the original document. It is reliable (selected sentences are always factually accurate, since they are taken directly from the source), computationally efficient, and interpretable (you can see exactly which sentences were chosen and why). The limitation is inflexibility: the summary can only contain sentences that exist in the source, which may produce a summary that is repetitive or misses connections between distant passages. Abstractive summarization generates new sentences that paraphrase and condense the source content — similar to how a human would summarize. Modern transformer models like BART and T5, fine-tuned on summarization datasets, produce high-quality abstractive summaries. However, they occasionally generate facts not present in the source, which is a serious limitation for applications where factual accuracy is critical.

Practical Guidance: Choosing the Right Approach

For keyword extraction, YAKE or KeyBERT (which uses sentence transformer embeddings to select keywords semantically representative of the document's content) are generally the best starting points for new applications. For summarization, the choice between extractive and abstractive depends primarily on your tolerance for factual errors. Legal, medical, and financial applications should use extractive approaches where factual accuracy can be guaranteed. News summarization, content recommendation, and consumer-facing applications can benefit from abstractive models' more natural output if they include appropriate disclaimers or human review processes. For all approaches, evaluate performance on a sample of your actual data rather than benchmark datasets — performance can vary significantly across domains.

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