before_sent:  Therefore, an attention-prediction model is constructed to learn the dependency between attention weights and sources. To apply the attention-aware inference on multi-document summarization, a Hierarchical Transformer (HT) is developed to accept lengthy inputs at the same time project cross-document information. Experiments on WikiSum \mbox{%DIFAUXCMD liu2018generating By refining the regular beam search with the attention-aware inference , significant improvements on the quality of summaries could be further observed.
before_sent_with_intent: <clarity>  Therefore, an attention-prediction model is constructed to learn the dependency between attention weights and sources. To apply the attention-aware inference on multi-document summarization, a Hierarchical Transformer (HT) is developed to accept lengthy inputs at the same time project cross-document information. Experiments on WikiSum \mbox{%DIFAUXCMD liu2018generating By refining the regular beam search with the attention-aware inference , significant improvements on the quality of summaries could be further observed.
after_sent:  Therefore, an attention-prediction model is constructed to learn the dependency between the optimal attention distribution and the source. By refining the regular beam search with the attention-aware inference , significant improvements on the quality of summaries could be further observed.