Keyphrase Generation Based on Deep Seq2seq Model

Keyphrase can provide highly summative information which can help us improve information utilization efficiency in the era of information overload. Though previous researches about keyphrase generation have provided some workable solutions, they generate keyphrase by ranking and selecting meaningful words from the source text. These approaches belong to an extractive method, by which they cannot effectively use semantic meaning of the source text, and are unable to generate keyphrases which do not appear in the source text. So we propose a sequence-to-sequence framework with attention mechanism, copy mechanism, and coverage mechanism, which can effectively deal with the above-mentioned drawbacks. The experimental results on five data sets reveal that our proposed model can achieve a better performance than the traditional extraction approaches and can also generate absent keyphrases which do not appear in the source text.