Improving Abstractive Summarization via Dilated Convolution
Dawei Jin, Ruizhi Kang, Hongjun Zhang, Wening Hao, Gang Chen · Journal of Physics Conference Series · 2020
Abstract In this paper, a sequence-to-sequence based hybrid neural network model is proposed for abstractive summarization. Our method utilizes Bi-directional Long Short-Term Memory (Bi-LSTM) and multi-level dilated convolutions (MDC) to capture the global semantic information and semantic-unit level information, respectively. In decoding phrase, our model generates words according to summary relevant information captured by attention mechanism. Experiment shows that this proposed model outperforms several strong baselines on both of Gigawords corpus and DUC-2004 task.