Short Texts Keyword Extraction
TL;DR
The study proposed an attentive autoencoder framework that integrated a multihead self-attention mechanism for contextual representation, an importance scoring network for keyword ranking, and a dual-loss optimization function combining reconstruction loss and sparsity loss to enable accurate keyword identification without manual annotation.
Abstract
This paper presents a case study of developing a self-supervised keyword extraction system for Wanfang Data, a leading Chinese academic database platform facing the challenge of tagging millions of short-article titles without access to manually labeled data. The study proposed an attentive autoencoder framework that integrated a multihead self-attention mechanism for contextual representation, an importance scoring network for keyword ranking, and a dual-loss optimization function combining reconstruction loss and sparsity loss to enable accurate keyword identification without manual annotation. Using 7,278 real article titles from Wanfang's production environment as the test corpus, the study evaluated the system against five baseline methods. The results showed that the proposed approach achieved an F1-score of 0.3581 and a mean average precision of 0.4544 for top-three keyword extraction, outperforming all baselines and demonstrating 15–20% improvement in ranking accuracy on very short texts (tokens < 10).