KEA: Practical Automatic Keyphrase Extraction

TL;DR

Kea is a practical machine learning-based algorithm for automatic keyphrase extraction from text, utilizing lexical candidate identification and a trained prediction model to effectively summarize document content.

Contribution

The paper introduces Kea, a simple, robust, and publicly available system that improves keyphrase extraction using lexical methods and machine learning.

Findings

High accuracy in identifying author-assigned keyphrases

Effective on large test corpus

Robust across different document types

Abstract

Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machine-learning algorithm to predict which candidates are good keyphrases. The machine learning scheme first builds a prediction model using training documents with known keyphrases, and then uses the model to find keyphrases in new documents. We use a large test corpus to evaluate Kea's effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available.