Better Highlighting: Creating Sub-Sentence Summary Highlights (2020)

Abstract: 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.

## introduction a summary is reliable only if it is true-to-original. abstractive summarizers are considered to be less reliable despite their impressive performance on benchmark datasets, because they can hallucinate facts and struggle to keep the original meanings intact @xcite . in this paper, we seek to generate summary highlights to be overlaid on the original documents to allow summaries to be understood in context and avoid misdirecting readers to false conclusions. this is especially important in areas involving legislation, political speeches, public policies, social media, and more @xcite . highlighting is most commonly used in education to make important information stand out and bring attention of readers to the essential topics @xcite . ## related work an abstract failing to retain the original meaning poses a substantial risk of harm to applications. abstractive summarizers can copy words from source documents or generate new words @xcite com/ucfnlp/better-highlighting ## our method we present a new method to identify self-contained segments, then select important and non-redundant segments to form a summary, as text fragments containing incomplete and disorganized information are hardly successful summary highlights. ## conclusion we make a first attempt to create sub-sentence summary highlights that are understandable and require minimum information from the surrounding context. highlighting is important to help readers sift through a large amount of texts and quickly grasp the main points. we describe a novel methodology to generate a rich set of self-contained segments from the documents, then use determinantal point processes to identify summary highlights. the method can be extended to other text genres such as public policies to aid reader comprehension, which will be our future work to explore. g. vladutz. 1983. natural language text segmentation techniques applied to the automatic compilation of printed subject indexes and for online database access. in first conference on applied natural language processing, pages 136-142, santa monica, california, usa. association for computational linguistics. kristian woodsend and mirella lapata. 2010. automatic generation of story highlights. in proceedings of the 48th annual meeting of the association for computational linguistics, pages 565-574, uppsala, sweden. association for computational linguistics. zhilin yang, zihang dai, yiming yang, jaime carbonell, russ r salakhutdinov, and quoc v le. 2019. xlnet: generalized autoregressive pretraining for language understanding. in h. wallach, h. larochelle, a. beygelzimer, f. d'alché-buc, e. fox, and r. garnett, editors, advances in neural information processing systems 32, pages 5753-5763. curran associates, inc.