Distraction-Based Neural Networks for Document SummarizationPublished in IJCAI-2016: the 25th International Joint Conference on Artificial Intelligence.
Table 3: Results on two subsets of the CNN datasets with different document lengths.
['[EMPTY]', 'Rouge-1', 'Rouge-2', 'Rouge-L']
Performance on different lengths of documents To observe the effectiveness of the distraction model over different document lengths, we further selected all short documents from the CNN training dataset into a subset (subset-1) with average length at 335 word tokens, and a subset of data (subset-2) that have the same number of documents as the subset-1, with averaged document length at 680 word tokens. The relative improvement is 29.0%, 25.6%, and 10.8%, compared with 25.9%, 20.5%, and 8.1% on subset-1, respectively. In general the best performance on both dataset is lower than that using all training data, suggesting using more training data can improve summarization performance.