News Article Teaser Tweets and How to Generate Them
Table 9: Rouge F1 scores for seq2seq model and seq2seq_point models on the teaser task.
['[EMPTY]', '[BOLD] Validation Rouge-1', '[BOLD] Validation Rouge-2', '[BOLD] Validation Rouge-L']
These models are then trained and evaluated on the teaser corpus obtained using Algorithm. We then sample 255k instances that have all associated short texts in them. The sampled corpus is split into non-overlapping 250k, 2k and 2k sets for training, validation, and testing, respectively. The split is constructed such that training, validation and test sets have equal representation of all eight domains. Any instances that describe events that were also described in training are removed from validation and test sets; thus, instances encountered in validation / test are quite distinct from instances encountered in training. Models were selected based on their performance on the validation set. Clearly, seq2seq_point performs better than seq2seq due to the boost in the recall gained by copying source words through the pointer network.