Keyword Extraction Explorer Tool

All six methods here are statistical or graph-based. They need no training data and work directly from the words in your text. They differ in how they decide which words matter.

YAKE

(statistical):

Scores each word using simple text features — how often it appears, where it sits, whether it's capitalised, and what surrounds it. Good all-rounder, works on short texts and many languages.

TF-IDF

(statistical):

Picks out words that are common in your text but rare in general — the ones that make it distinctive. A long-standing, dependable baseline.

TopicRank

(graph-based):

Groups similar words into topics first, then ranks the topics and picks one phrase to represent each. Reduces near-duplicates, so results cover a broader spread of themes.

MultipartiteRank

(graph-based):

A refinement of TopicRank that keeps individual phrases as the unit while still respecting topics. Tends to give the most balanced, precise keyphrases (the strongest performer in our tests).

PositionRank

(graph-based):

Gives extra weight to words that appear early in the text, on the idea that important terms are often introduced up front. Useful for structured writing like articles and abstracts.

TextRank

(graph-based):

Builds a network of words that appear near each other and ranks them using the same idea as Google's PageRank — a word is important if other important words connect to it.

TopicRank, MultipartiteRank, PositionRank and TextRank are graph-based methods provided by the pke library.