In contrast to skip-thought vectors (Kiros et al., 2015) which are learned by reconstructing the surrounding sentences of an encoded sentence, we borrow the idea of negative sampling from word representation learning (Mikolov et al., 2013).
for a sentence s i in document D, we take its previous sentence s i−1 and its following sentence s i+1 to be positive examples, and consider any other sentence in the corpus to be a negative example.