Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure

Bohan Zhang, Prafulla Kumar Choubey, Ruihong Huang


Abstract
Document-level text simplification often deletes some sentences besides performing lexical, grammatical or structural simplification to reduce text complexity. In this work, we focus on sentence deletions for text simplification and use a news genre-specific functional discourse structure, which categorizes sentences based on their contents and their function roles in telling a news story, for predicting sentence deletion. We incorporate sentence categories into a neural net model in two ways for predicting sentence deletions, either as additional features or by jointly predicting sentence deletions and sentence categories. Experimental results using human-annotated data show that incorporating the functional structure improves the recall of sentence deletion prediction by 6.5% and 10.7% respectively using the two methods, and improves the overall F1-score by 3.6% and 4.3% respectively.
Anthology ID:
2022.acl-short.28
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
255–261
Language:
URL:
https://aclanthology.org/2022.acl-short.28
DOI:
10.18653/v1/2022.acl-short.28
Bibkey:
Cite (ACL):
Bohan Zhang, Prafulla Kumar Choubey, and Ruihong Huang. 2022. Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 255–261, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure (Zhang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-short.28.pdf
Data
Newsela