In this paper, we study automatic keyphrase generation. Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting in duplication and coverage issues. To solve these problems, we propose a new sequence-to-sequence architecture for keyphrase generation named CorrRNN, which captures correlation among multiple keyphrases in two ways. First, we employ a coverage vector to indicate whether the word in the source document has been summarized by previous phrases to improve the coverage for keyphrases. Second, preceding phrases are taken into account to eliminate duplicate phrases and improve result coherence. Experiment results show that our model significantly outperforms the state-of-the-art method on benchmark datasets in terms of both accuracy and diversity.
Chen et al. REF modeled correlation among multiple keywords in an end-to-end fashion to eliminate duplicate keywords and improve result coherence.
Keyphrase Generation with Correlation Constraints
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Computer Science