关键词: 生成式文本摘要, 局部注意力, 序列到序列框架, 覆盖机制

Abstract:

Attention-based encoder-decoder abstractive summarization methods have been widely used for auto text summarization. However, these methods suffer from three shortcomings：usually producing semantic irrelevance, repeated phrase sentences and Out-Of-Vocabulary（OOV） words. In this work, it proposes an abstractive summarization method, DAPC model that combines dual attention, pointer-generator network and coverage mechanism based on typical attention-based encoder-decoder framework to solve these problems. First, it uses local attention and convolutional neural network to obtain the deep hidden [n]-gram language features. Then, based on the typical encoder-decoder with attention model, it adds a pointer-generator network that through the pointing mechanism to copy words from source text, and solves the OOV problem. Last, coverage mechanism is used to solve the problem of repetition. Experiments on non-anonymized CNN/Daily Mail prove that the model has high semantic relevance to the source text and owns the capacity to reduce repetition.

Key words: abstractive summarization, local attention, sequence-to-sequence framework, coverage mechanism