  Keyphrases, that concisely summarize the high-level topics discussed in a
document, can be categorized into present keyphrase which explicitly appears in
the source text, and absent keyphrase which does not match any contiguous
subsequence but is highly semantically related to the source. Most existing
keyphrase generation approaches synchronously generate present and absent
keyphrases without explicitly distinguishing these two categories. In this
paper, a Select-Guide-Generate (SGG) approach is proposed to deal with present
and absent keyphrase generation separately with different mechanisms.
Specifically, SGG is a hierarchical neural network which consists of a
pointing-based selector at low layer concentrated on present keyphrase
generation, a selection-guided generator at high layer dedicated to absent
keyphrase generation, and a guider in the middle to transfer information from
selector to generator. Experimental results on four keyphrase generation
benchmarks demonstrate the effectiveness of our model, which significantly
outperforms the strong baselines for both present and absent keyphrases
generation. Furthermore, we extend SGG to a title generation task which
indicates its extensibility in natural language generation tasks.
