Hierarchical Transformers for Multi-Document Summarization
Table 1: ROUGE-L recall against target summary for L′-best paragraphs obtained with tf-idf cosine similarity and our ranking model.
['Methods', 'ROUGE-L Recall', 'ROUGE-L Recall', 'ROUGE-L Recall', 'ROUGE-L Recall']
S4SS0SSS0Px2 Paragraph Ranking The hidden size of the two LSTMs was set to 256, and dropout (with dropout probability of 0.2) was used before all linear layers. Adagrad Duchi et al. We compare our ranking model against the method proposed in Liu et al. We take the first L′ paragraphs from the ordered paragraph set produced by our ranker and the similarity-based method, respectively. We concatenate these paragraphs and calculate their ROUGE-L recall against the gold target text. We can see that our ranker effectively extracts related paragraphs and produces more informative input for the downstream summarization task.