Improving Human Text Simplification with Sentence Fusion

Max Schwarzer, Teerapaun Tanprasert, David Kauchak


Abstract
The quality of fully automated text simplification systems is not good enough for use in real-world settings; instead, human simplifications are used. In this paper, we examine how to improve the cost and quality of human simplifications by leveraging crowdsourcing. We introduce a graph-based sentence fusion approach to augment human simplifications and a reranking approach to both select high quality simplifications and to allow for targeting simplifications with varying levels of simplicity. Using the Newsela dataset (Xu et al., 2015) we show consistent improvements over experts at varying simplification levels and find that the additional sentence fusion simplifications allow for simpler output than the human simplifications alone.
Anthology ID:
2021.textgraphs-1.10
Volume:
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Editors:
Alexander Panchenko, Fragkiskos D. Malliaros, Varvara Logacheva, Abhik Jana, Dmitry Ustalov, Peter Jansen
Venue:
TextGraphs
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–114
Language:
URL:
https://aclanthology.org/2021.textgraphs-1.10
DOI:
10.18653/v1/2021.textgraphs-1.10
Bibkey:
Cite (ACL):
Max Schwarzer, Teerapaun Tanprasert, and David Kauchak. 2021. Improving Human Text Simplification with Sentence Fusion. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pages 106–114, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Improving Human Text Simplification with Sentence Fusion (Schwarzer et al., TextGraphs 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.textgraphs-1.10.pdf
Data
Newsela