Research Area: Machine Learning
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.
Keywords:
Keyphrase Generation
feature extraction
Deep Seq2seq Model
attention mechanism
coverage mechanism
copy mechanism
Machine Learning
Deep Learning
Author(s) Name: Yong Zhang; Weidong Xiao
Journal name: IEEE Access
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/ACCESS.2018.2865589
Volume Information: Volume: 6, Page(s): 46047 - 46057
