A Reinforced Topic-Aware Convolutional Sequence-to-SequenceModel for Abstractive Text Summarization
Table 2: Accuracy on the Gigaword corpus in terms of the full-length ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). Best performance on each score is displayed in boldface.
['[EMPTY]', '[BOLD] RG-1 (F)', '[BOLD] RG-2 (F)', '[BOLD] RG-L (F)']
We demonstrate the effectiveness of our proposed model via a step-by-step justification. First, the basic ConvS2S structure with topic-aware model or reinforcement learning is tested, respectively. Then we combine the two to show the performance of our Reinforced-Topic-ConvS2S model. The ABS and ABS+ models are attention-based neural models for text summarization. The RAS-Elman model introduces a conditional RNN, in which the conditioner is provided by a convolutional attention-based encoder. The words-lvt5k-1sent model is also a RNN-based attention model which implements a large-vocabulary trick. Besides, RNN+MRT employs the minimum risk training strategy which directly optimizes model parameters in sentence level with respect to the evaluation metrics. SEASS (beam) extends the sequence-to-sequence framework with a selective encoding model. The results have demonstrated that both the topic-aware module and the reinforcement learning process can improve the accuracy on text summarization. Moreover, our proposed model exhibits best scores of RG-1, RG-2 and RG-L.