before_sent:  By refining the vanilla beam searchwith the attention-aware mechanism, significant improvements on the quality of summaries could be observed. Last but not the least, the attention-aware inference has strong universality that can be easily adopted to different hierarchical summarization models to promote the models' performance .
before_sent_with_intent: <meaning-changed>  By refining the vanilla beam searchwith the attention-aware mechanism, significant improvements on the quality of summaries could be observed. Last but not the least, the attention-aware inference has strong universality that can be easily adopted to different hierarchical summarization models to promote the models' performance .
after_sent:  By refining the vanilla beam searchwith the attention-aware inference produces summaries rather differently from the beam search, and achieves promising improvements of higher scores and greater conciseness. The algorithm is also proven robust as it remains to outperform beam search significantly even with corrupted attention distributions .