Global-aware Beam Search for Neural Abstractive SummarizationDownload PDF

May 21, 2021 (edited Jan 21, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: beam search, global optimal, global attention distribution, summarization, sequence-to-sequence
  • TL;DR: A calibrated beam-based algorithm with awareness of the global attention distribution
  • Abstract: This study develops a calibrated beam-based algorithm with awareness of the global attention distribution for neural abstractive summarization, aiming to improve the local optimality problem of the original beam search in a rigorous way. Specifically, a novel global protocol is proposed based on the attention distribution to stipulate how a global optimal hypothesis should attend to the source. A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion. This novel design enjoys a distinctive property, i.e., the global attention distribution could be predicted before inference, enabling step-wise improvements on the beam search through the global scoring mechanism. Extensive experiments on nine datasets show that the global (attention)-aware inference significantly improves state-of-the-art summarization models even using empirical hyper-parameters. The algorithm is also proven robust as it remains to generate meaningful texts with corrupted attention distributions. The codes and a comprehensive set of examples are available.
  • Supplementary Material: pdf
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  • Code: https://github.com/yema2018/global_aware
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