论文标题
学会将句子与变压器融合以进行摘要
Learning to Fuse Sentences with Transformers for Summarization
论文作者
论文摘要
融合句子的能力对于汇总系统非常有吸引力，因为它是生成简洁摘要的重要步骤。但是，迄今为止，摘要者可能无法通过融合句子。他们倾向于通过融合或产生不正确的融合来产生摘要句子，从而导致摘要无法保留原始含义。在本文中，我们探讨了变形金刚融合句子并提出新颖算法来增强其执行句子融合的能力的能力，通过利用句子之间的对应点知识来增强其执行句子融合的能力。通过广泛的实验，我们研究了不同设计选择对变压器性能的影响。我们的发现突出了建模句子之间对应点对有效句子融合的重要性。
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce few summary sentences by fusion or generate incorrect fusions that lead the summary to fail to retain the original meaning. In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences. Through extensive experiments, we investigate the effects of different design choices on Transformer's performance. Our findings highlight the importance of modeling points of correspondence between sentences for effective sentence fusion.
