Data-driven sentence simplification: survey and benchmark
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Published version
Author(s)
Alva-Manchego, Fernando
Scarton, Carolina
Specia, Lucia
Type
Journal Article
Abstract
Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand. In order to do so, several rewriting transformations can be performed such as replacement, reordering, and splitting. Executing these transformations while keeping sentences grammatical, preserving their main idea, and generating simpler output, is a challenging and still far from solved problem. In this article, we survey research on SS, focusing on approaches that attempt to learn how to simplify using corpora of aligned original-simplified sentence pairs in English, which is the dominant paradigm nowadays. We also include a benchmark of different approaches on common data sets so as to compare them and highlight their strengths and limitations. We expect that this survey will serve as a starting point for researchers interested in the task and help spark new ideas for future developments.
Date Issued
2020-03-01
Date Acceptance
2019-09-15
Citation
Computational Linguistics, 2020, 46 (1), pp.135-187
ISSN
0891-2017
Publisher
MIT Press
Start Page
135
End Page
187
Journal / Book Title
Computational Linguistics
Volume
46
Issue
1
Copyright Statement
© 2020 Association for Computational Linguistics
Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0) license
Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0) license
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000530333100005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Social Sciences
Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Linguistics
Language & Linguistics
Computer Science
TEXT SIMPLIFICATION
READABILITY
ALGORITHMS
Publication Status
Published
Date Publish Online
2020-04-02