论文标题
汇总候选人融合
Towards Summary Candidates Fusion
论文作者
论文摘要
序列到序列的深度神经模型进行了微调，用于抽象性摘要可以在数据集上具有足够的人类注释的良好性能。然而，已经证明他们尚未达到全部潜力，在顶部梁搜索输出和甲骨文梁之间存在较大差距。最近，已经提出了重新排列的方法，以学习选择一个更好的摘要候选人。但是，这种方法受到第一阶段候选人捕获的摘要质量方面的限制。为了绕过这一限制，我们提出了一个名为SumpAfusion的第二阶段抽象摘要中的新范式，该范例融合了几个摘要候选者，以产生新的抽象性二阶摘要。我们的方法在几个摘要数据集上效果很好，从而提高了融合摘要的胭脂分数和定性属性。当候选人融合更糟时，这特别好，例如，在我们设置新的最新设置的几杆设置中。我们将在https://github.com/ntunlp/summafusion/上提供代码和检查点。
Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap between the top beam search output and the oracle beam. Recently, re-ranking methods have been proposed, to learn to select a better summary candidate. However, such methods are limited by the summary quality aspects captured by the first-stage candidates. To bypass this limitation, we propose a new paradigm in second-stage abstractive summarization called SummaFusion that fuses several summary candidates to produce a novel abstractive second-stage summary. Our method works well on several summarization datasets, improving both the ROUGE scores and qualitative properties of fused summaries. It is especially good when the candidates to fuse are worse, such as in the few-shot setup where we set a new state-of-the-art. We will make our code and checkpoints available at https://github.com/ntunlp/SummaFusion/.
