# Better Highlighting: Creating Sub-Sentence Summary Highlights

arXiv: https://arxiv.org/abs/2010.10566 · HTML: https://mlsanity.com/paper/2010.10566/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge](https://mlsanity.com/paper/2403.01432/) (2024)
- [OASum: Large-Scale Open Domain Aspect-based Summarization](https://mlsanity.com/paper/2212.09233/) (2022)
- [Proposition-Level Clustering for Multi-Document Summarization](https://mlsanity.com/paper/2112.08770/) (2021)
- [TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts](https://mlsanity.com/paper/2110.01159/) (2021)
- [Controlled Text Reduction](https://mlsanity.com/paper/2210.13449/) (2022)
- [TopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue System](https://mlsanity.com/paper/2109.05187/) (2021)
- [Personal Salience: Highlighting Is Social, but Individuality Lives in Selection](https://mlsanity.com/paper/2606.09024/) (2026)
- [EDU-level Extractive Summarization with Varying Summary Lengths](https://mlsanity.com/paper/2210.04029/) (2022)
- [Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan](https://mlsanity.com/paper/2209.10041/) (2022)
- [A Unifying Scheme for Extractive Content Selection Tasks](https://mlsanity.com/paper/2507.16922/) (2025)
- [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025)

## Abstract (arXiv)

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.