Empower Keywords Generation for Short Texts with Graph-to-Sequence Learning

Abstract

Keywords are useful in natural language tasks. However, it is a challenge task to extraction keywords from short texts. In which the model may be subject to impaction of topic dependence and poor text organization structure. To resolve this limitation, we propose a keywords generation model ADGCN of short texts based on graph-to-sequence learning. The model to jointly short texts contextual feature and positional feature based adaptation for this task. We learn domain-invariant feature representations by using graph-building feature and node topic feature space, and jointly perform linear generate feature in framework of keywords decoding. Experiment results on real social datasets demonstrate that our proposed model achieves impressive empirical performance on relevance, information and coherence. Besides, the proposed ADGCN also outperforms the state-of-the-arts on public KP20k dataset. The experiments testify that the model can generate the topic keywords of short texts and effectively alleviate the influence of data disturbance.