  Keyphrase provides highly-condensed information that can be effectively used
for understanding, organizing and retrieving text content. Though previous
studies have provided many workable solutions for automated keyphrase
extraction, they commonly divided the to-be-summarized content into multiple
text chunks, then ranked and selected the most meaningful ones. These
approaches could neither identify keyphrases that do not appear in the text,
nor capture the real semantic meaning behind the text. We propose a generative
model for keyphrase prediction with an encoder-decoder framework, which can
effectively overcome the above drawbacks. We name it as deep keyphrase
generation since it attempts to capture the deep semantic meaning of the
content with a deep learning method. Empirical analysis on six datasets
demonstrates that our proposed model not only achieves a significant
performance boost on extracting keyphrases that appear in the source text, but
also can generate absent keyphrases based on the semantic meaning of the text.
Code and dataset are available at
https://github.com/memray/OpenNMT-kpg-release.
