Experiments on two real-world datasets demonstrate that our proposed model can significantly outperform the state-of-the-art extractive methods on summary quality, coverage and non-redundancy.
bidirectional LSTMs (BiL-STMs) on word-and sentence-level are first used to encode a document with N sentences into Second, a global featureS ∈ R ds is computed as an average of all the sentence vectors.