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.

本文探讨了利用 Transformers 中的点对点对应关系增强句子融合能力的算法，通过大量实验发现，建模句子之间的对应点对于有效的句子融合至关重要。

使用 Transformer 融合句子进行摘要学习

Learning to Fuse Sentences with Transformers for Summarization

Fusing sentences containing disparate content is a remarkable human ability
that helps create informative and succinct summaries. Such a simple task for
humans has remained challenging for modern abstractive summarizers,
substantially restricting their applicability in real-world scenarios. In this
paper, we present an investigation into fusing sentences drawn from a document
by introducing the notion of points of correspondence, which are cohesive
devices that tie any two sentences together into a coherent text. The types of
points of correspondence are delineated by text cohesion theory, covering
pronominal and nominal referencing, repetition and beyond. We create a dataset
containing the documents, source and fusion sentences, and human annotations of
points of correspondence between sentences. Our dataset bridges the gap between
coreference resolution and summarization. It is publicly shared to serve as a
basis for future work to measure the success of sentence fusion systems.
(this https URL)

本文介绍了一种利用文本连贯性理论中的对应点将文档中的句子融合成一篇连贯文本的方法，并创建了数据集以促进此技术的发展和测量。