Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Table 4: ROUGE results on XSum test set. We report ROUGE-1 (R1), ROUGE-2 (R2), and ROUGE-L (RL) F1 scores. Extractive systems are in the upper block, RNN-based abstractive systems are in the middle block, and convolutional abstractive systems are in the bottom block.
['Models', 'R1', 'R2', 'RL']
Unigram and bigram overlap (ROUGE-1 and ROUGE-2) are a proxy for assessing informativeness and the longest common subsequence (ROUGE-L) represents fluency. We hypothesize that its superior performance stems from the ability to better represent document content (i.e., by capturing long-range dependencies). We also experimented with various decoders by conditioning every prediction on the topic of the document, basically encouraging the summary to be in the same theme as the document (dectD) or letting the decoder decide the theme of the summary. Interestingly, all four T-ConvS2S variants outperform ConvS2S. T-ConvS2S performs best when both encoder and decoder are constrained by the document topic (enc(t′,tD),dectD). In the remainder of the paper, we refer to this variant as T-ConvS2S.