@inproceedings{bakker-kamps-2024-beyond,
title = "Beyond Sentence-level Text Simplification: Reproducibility Study of Context-Aware Document Simplification",
author = "Bakker, Jan and
Kamps, Jaap",
editor = "Nunzio, Giorgio Maria Di and
Vezzani, Federica and
Ermakova, Liana and
Azarbonyad, Hosein and
Kamps, Jaap",
booktitle = "Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.determit-1.3",
pages = "27--38",
abstract = "Previous research on automatic text simplification has focused on almost exclusively on sentence-level inputs. However, the simplification of full documents cannot be tackled by naively simplifying each sentence in isolation, as this approach fails to preserve the discourse structure of the document. Recent Context-Aware Document Simplification approaches explore various models whose input goes beyond the sentence-level. These model achieve state-of-the-art performance on the Newsela-auto dataset, which requires a difficult to obtain license to use. We replicate these experiments on an open-source dataset, namely Wiki-auto, and share all training details to make future reproductions easy. Our results validate the claim that models guided by a document-level plan outperform their standard counterparts. However, they do not support the claim that simplification models perform better when they have access to a local document context. We also find that planning models do not generalize well to out-of-domain settings. Lay Summary: We have access to unprecedented amounts of information, yet the most authoritative sources may exceed a user{'}s language proficiency level. Text simplification technology can change the writing style while preserving the main content. Recent paragraph-level and document-level text simplification approaches outcompete traditional sentence-level approaches, and increase the understandability of complex texts.",
}
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<abstract>Previous research on automatic text simplification has focused on almost exclusively on sentence-level inputs. However, the simplification of full documents cannot be tackled by naively simplifying each sentence in isolation, as this approach fails to preserve the discourse structure of the document. Recent Context-Aware Document Simplification approaches explore various models whose input goes beyond the sentence-level. These model achieve state-of-the-art performance on the Newsela-auto dataset, which requires a difficult to obtain license to use. We replicate these experiments on an open-source dataset, namely Wiki-auto, and share all training details to make future reproductions easy. Our results validate the claim that models guided by a document-level plan outperform their standard counterparts. However, they do not support the claim that simplification models perform better when they have access to a local document context. We also find that planning models do not generalize well to out-of-domain settings. Lay Summary: We have access to unprecedented amounts of information, yet the most authoritative sources may exceed a user’s language proficiency level. Text simplification technology can change the writing style while preserving the main content. Recent paragraph-level and document-level text simplification approaches outcompete traditional sentence-level approaches, and increase the understandability of complex texts.</abstract>
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%0 Conference Proceedings
%T Beyond Sentence-level Text Simplification: Reproducibility Study of Context-Aware Document Simplification
%A Bakker, Jan
%A Kamps, Jaap
%Y Nunzio, Giorgio Maria Di
%Y Vezzani, Federica
%Y Ermakova, Liana
%Y Azarbonyad, Hosein
%Y Kamps, Jaap
%S Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F bakker-kamps-2024-beyond
%X Previous research on automatic text simplification has focused on almost exclusively on sentence-level inputs. However, the simplification of full documents cannot be tackled by naively simplifying each sentence in isolation, as this approach fails to preserve the discourse structure of the document. Recent Context-Aware Document Simplification approaches explore various models whose input goes beyond the sentence-level. These model achieve state-of-the-art performance on the Newsela-auto dataset, which requires a difficult to obtain license to use. We replicate these experiments on an open-source dataset, namely Wiki-auto, and share all training details to make future reproductions easy. Our results validate the claim that models guided by a document-level plan outperform their standard counterparts. However, they do not support the claim that simplification models perform better when they have access to a local document context. We also find that planning models do not generalize well to out-of-domain settings. Lay Summary: We have access to unprecedented amounts of information, yet the most authoritative sources may exceed a user’s language proficiency level. Text simplification technology can change the writing style while preserving the main content. Recent paragraph-level and document-level text simplification approaches outcompete traditional sentence-level approaches, and increase the understandability of complex texts.
%U https://aclanthology.org/2024.determit-1.3
%P 27-38
Markdown (Informal)
[Beyond Sentence-level Text Simplification: Reproducibility Study of Context-Aware Document Simplification](https://aclanthology.org/2024.determit-1.3) (Bakker & Kamps, DeTermIt-WS 2024)
ACL