For a sentence, we first apply word embedding matrix to translate the raw words in the sentence into distributional representations, and then adopt bidirectional LSTMs to learn a sentence-level representation for summarizing its high level semantic concepts.
assume that a sentence , these words are transformed to dense vectors through a word embedding matrix W e : is then fed into bidirectional LSTMs sequentially to compute a semantic representation of the sentence.