Paragraph level Attention Aware Inference for Multi Document Neural Abstractive Summarization.
Inspired by Google's Neural Machine Translation (NMT) that models the one to one alignment in translation tasks with an uniform attention distribution during the inference, this study proposes an attention aware inference algorithm for Neural Abstractive Summarization (NAS) to regulate generated summaries to attend to source contents with the optimal coverage. Unlike NMT, NAS is not based on one to one transformation. Instead, its attention distribution for the input should be irregular and depend on the content layout of the source documents. To address this matter, we construct an attention prediction model to learn the dependency between the optimal attention distribution and the source. By refining the vanilla beam search with 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.