In rough reading, we aim to form a general cognition on a given document which encodes the document into sentence embeddings as well as produces the feature set F including global and local features.
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.