Efficient summarization with read-again and copy mechanism
Table 2: Rouge-N limited-length recall on DUC2004. Size denotes the size of decoder vocabulary in a model.
['Models', 'Size', 'Rouge-1', 'Rouge-2', 'Rouge-L']
Evaluation on DUC2004: DUC 2004 (Over et al. Each article is paired with 4 different human-generated reference summaries, capped at 75 characters. This dataset is evaluation-only. Similar to Rush et al. , we train our neural model on the Gigaword training set, and show the models’ performances on DUC2004. Following the convention, we also use ROUGE limited-length recall as our evaluation metric, and set the capping length to 75 characters. We generate summaries with 15 words using beam-size of 10. Furthermore, our model only uses 15k decoder vocabulary, while previous methods use 69k or 200k.