On the other hand, <cite>Noraset et al. (2017)</cite> attempted to generate a definition of a word from its word embedding induced from massive text, followed by Gadetsky et al. (2018) that refers to a local context to define a polysemous word with a local context by choosing relevant dimensions of their embeddings. Although these research efforts revealed that both local and global contexts of words are useful in generating their definitions, none of these studies exploited both local and global contexts directly. In this study, we tackle a task of describing (defining) a phrase when given its local context as (Ni and Wang, 2017) , while allowing access to other usage examples via word embeddings trained from massive text (global contexts) (Noraset et al., 2017; Gadetsky et al., 2018) .