  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.
