Amongst the best means to summarize is highlighting. In this paper, we aim to
generate summary highlights to be overlaid on the original documents to make it
easier for readers to sift through a large amount of text. The method allows
summaries to be understood in context to prevent a summarizer from distorting
the original meaning, of which abstractive summarizers usually fall short. In
particular, we present a new method to produce self-contained highlights that
are understandable on their own to avoid confusion. Our method combines
determinantal point processes and deep contextualized representations to
identify an optimal set of sub-sentence segments that are both important and
non-redundant to form summary highlights. To demonstrate the flexibility and
modeling power of our method, we conduct extensive experiments on summarization
datasets. Our analysis provides evidence that highlighting is a promising
avenue of research towards future summarization.

本文旨在通过生成可叠加在原始文档上的概要亮点的方式来减轻读者处理大量文本的负担。我们提出了一种新方法，通过联合确定点过程和深层语境表示来产生自包含亮点，以避免混淆并保持原意的完整性，以展示我们的方法的灵活性和建模能力。该方法在概括数据集上进行了广泛的实验，证明亮点是未来概括研究的有前途的方向。