Abstractive Text Summarization Based on Semantic Alignment Network

Aiming at the problem of insufficient utilization of the overall semantic information of abstracts in decoding by the currently abstractive summarization model, this paper proposes a neural network automatic abstract model based on semantic alignment. This model is based on the Sequence-to-Sequence model with attention, Pointer mechanism and Coverage mechanism. A semantic alignment network is added between the encoder and the decoder to achieve the semantic information alignment of the text to the abstract. The achieved semantic information is concatenated with the context vector in decoding, so that when the decoder predicts the vocabulary, it not only uses the partial semantics before decoding, but also considers the overall semantics of the digest sequence. Experiments on the Chinese news corpus LCSTS show that the proposed model can effectively improve the quality of abstractive summarization. © 2021 Peking University.