  Keyphrase Generation (KG) is the task of generating central topics from a
given document or literary work, which captures the crucial information
necessary to understand the content. Documents such as scientific literature
contain rich meta-sentence information, which represents the logical-semantic
structure of the documents. However, previous approaches ignore the constraints
of document logical structure, and hence they mistakenly generate keyphrases
from unimportant sentences. To address this problem, we propose a new method
called Sentence Selective Network (SenSeNet) to incorporate the meta-sentence
inductive bias into KG. In SenSeNet, we use a straight-through estimator for
end-to-end training and incorporate weak supervision in the training of the
sentence selection module. Experimental results show that SenSeNet can
consistently improve the performance of major KG models based on seq2seq
framework, which demonstrate the effectiveness of capturing structural
information and distinguishing the significance of sentences in KG task.
