Title-Guided Encoding for Keyphrase Generation


  • Wang Chen The Chinese University of Hong Kong
  • Yifan Gao The Chinese University of Hong Kong
  • Jiani Zhang The Chinese University of Hong Kong
  • Irwin King The Chinese University of Hong Kong
  • Michael R. Lyu The Chinese University of Hong Kong




Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoderdecoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a titleguided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.




How to Cite

Chen, W., Gao, Y., Zhang, J., King, I., & Lyu, M. R. (2019). Title-Guided Encoding for Keyphrase Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6268-6275. https://doi.org/10.1609/aaai.v33i01.33016268



AAAI Technical Track: Natural Language Processing