---
abstract: 'Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information. A good summary is characterized by language fluency and high information overlap with the source sentence. We model these two aspects in an unsupervised objective function, consisting of language modeling and semantic similarity metrics. We search for a high-scoring summary by discrete optimization. Our proposed method achieves a new state-of-the art for unsupervised sentence summarization according to [<span style="font-variant:small-caps;">Rouge</span>]{} scores. Additionally, we demonstrate that the commonly reported [<span style="font-variant:small-caps;">Rouge</span>]{} F1 metric is sensitive to summary length. Since this is unwillingly exploited in recent work, we emphasize that future evaluation should explicitly group summarization systems by output length brackets.[^1]'
author:
- |
    Raphael Schumann$^1$, Lili Mou$^2$, Yao Lu$^3$, Olga Vechtomova$^3$, Katja Markert$^1$\
    $^1$Institute of Computational Linguistics, Heidelberg University, Germany\
    `{rschuman, markert}@cl.uni-heidelberg.de`\
    $^2$Dept. Computing Science, University of Alberta, Canada; Alberta Machine Intelligence Institute (Amii)\
    `doublepower.mou@gmail.com`\
    $^3$University of Waterloo, Canada\
    `{yao.lu, ovechtom}@uwaterloo.ca`
bibliography:
- 'acl2020.bib'
title: 'Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction'
---

[^1]: Our code and system outputs are available at: <https://github.com/raphael-sch/HC_Sentence_Summarization>
