Before revision: 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 searchwith the attention-aware mechanism, significant improvements on the quality of summaries could be observed.
After revision: 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 seq-to-seq models. It corrects beam search step-by-step via the optimal attention distribution to make the generated text attend to source tokens in a controlled way. Experiments show the proposed attention-aware mechanism, significant improvements on the quality of summaries could be observed.