Topic-Guided Variational Autoencoders for Text Generation
Table 6: Results on Gigawords and DUC-2004.
['[BOLD] Methods', 'Gigawords  [BOLD] RF-1', 'Gigawords  [BOLD] RF-2', 'Gigawords  [BOLD] RF-L', 'DUC-2004  [BOLD] RR-1', 'DUC-2004  [BOLD] RR-2', 'DUC-2004  [BOLD] RR-L']
Results The results in It is worthwhile to note that recently several much more complex CNN/RNN architectures have been proposed for abstract text summarization, such as SEASS Zhou et al. ConvS2S Gehring et al. In this work, we focus on a relatively simple RNN architecture for fair comparison. In such a way, we are able to conclude that the improvements on the results are mainly from our topic-guided text generation strategy. As can be seen, though the Var-Seq2Seq model achieves comparable performance with the standard Seq2Seq model, the usage of Householder flow for more flexible posterior inference boosts the performance. Additionally, by combining the proposed topic-dependent prior and Householder flow, we yield further performance improvements, demonstrating the importance of topic guidance for text summarization.