Unsupervised Open-domain Keyphrase Generation
Lam Thanh Do Pritom Saha Akash Kevin Chen-Chuan Chang
In this work, we study the problem of unsupervised open-domain keyphrase
generation, where the objective is a keyphrase generation model that can be
built without using human-labeled data and can perform consistently across
domains. To solve this problem, we propose a seq2seq model that consists of two
modules, namely \textit{phraseness} and \textit{informativeness} module, both
of which can be built in an unsupervised and open-domain fashion. The
phraseness module generates phrases, while the informativeness module guides
the generation towards those that represent the core concepts of the text. We
thoroughly evaluate our proposed method using eight benchmark datasets from
different domains. Results on in-domain datasets show that our approach
achieves state-of-the-art results compared with existing unsupervised models,
and overall narrows the gap between supervised and unsupervised methods down to
about 16\%. Furthermore, we demonstrate that our model performs consistently
across domains, as it overall surpasses the baselines on out-of-domain
datasets.