Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Table 1: Performance comparison of various models. ’*’ indicates statistical significance of the corresponding model with respect to the baseline model on its dataset as given by the 95% confidence interval in the official Rouge script. We report statistical significance only for the best performing models. ’src. copy rate’ for the reference data on our validation sample is 45%. Please refer to Section 4 for explanation of notation.
['[BOLD] #', '[BOLD] Model name', '[BOLD] Rouge-1', '[BOLD] Rouge-2', '[BOLD] Rouge-L', '[BOLD] Src. copy rate (%)']
In addition, we also report the percentage of tokens in the system summary that occur in the source (which we call ‘src. words- lvt2k-2sent : This model is identical to the model above except for the fact that it is trained on the first two sentences from the source. We also tried adding more sentences, but the performance dropped, which is probably because the latter sentences in this corpus are not pertinent to the summary. words- feats- lvt2k-2sent In total, our embedding vector grew from the original 100 to 155, and produced incremental gains compared to its counterpart words-lvt2k-2sent feats- lvt2k-2sent-ptr: This is the switching generator/pointer model described in Sec. but in addition, we also use feature-rich embeddings on the document side as in the above model. Comparison with state-of-the-art: We also trained another system which we call words-lvt5k-1sent which has a larger LVT vocabulary size of 5k, but also has much larger source and target vocabularies of 400K and 200K respectively.