Attention-aware Inference: Generating Summarization Texts According to Attention Distributions

This paper proposes a novel 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 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.