On the Ethical Considerations of Text Simplification

Gooding, Sian

doi:10.18653/v1/2022.slpat-1.7

Cited by 10 publications

(10 citation statements)

References 27 publications

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“…In most cases, the participants of such comprehensibility studies are persons without disabilities or crowdworkers without specific inclusion criteria, who are not part of the primary target group of simplified language. This can be problematic, because what is considered difficult varies between reader groups [24,60], and the requirements for text simplification should not be considered universal [22]. Some exceptions of studies assessing ATS output among the target groups include experiments with deaf and hard-of-hearing adults [3], persons with intellectual disabilities [25,50] or dyslexia [46], and language learners [15].…”

Section: Related Work 21 Human Evaluation Of Automatic Text Simplific...mentioning

confidence: 99%

Digital Comprehensibility Assessment of Simplified Texts among Persons with Intellectual Disabilities

Säuberli,

Holzknecht,

Haller

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Text simplification refers to the process of increasing the comprehensibility of texts. Automatic text simplification models are most commonly evaluated by experts or crowdworkers instead of the primary target groups of simplified texts, such as persons with intellectual disabilities. We conducted an evaluation study of text comprehensibility including participants with and without intellectual disabilities reading unsimplified, automatically and manually simplified German texts on a tablet computer. We explored four different approaches to measuring comprehensibility: multiple-choice comprehension questions, perceived difficulty ratings, response time, and reading speed. The results revealed significant variations in these measurements, depending on the reader group and whether the text had undergone automatic or manual simplification. For the target group of persons with intellectual disabilities, comprehension questions emerged as the most reliable measure, while analyzing reading speed provided valuable insights into participants' reading behavior.

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Section: Related Work 21 Human Evaluation Of Automatic Text Simplific...mentioning

confidence: 99%

Digital Comprehensibility Assessment of Simplified Texts among Persons with Intellectual Disabilities

Säuberli,

Holzknecht,

Haller

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…TS is, therefore, useful in improving the vocabulary and literacy development of L2 learners [7], people with autism [151], dyslexia [35,131], or aphasia [35], as well as children [49]. However, Crossley et al [46] presents arguments for and against the use of simplified texts within L2 classrooms with Gooding [63] pointing out that the usefulness of simplified texts may vary between target demographics and in some instances may be inferior to alternative reading strategies. Despite this, throughout the years TS systems have assessed lexical complexity through a number of ways.…”

Section: Improving Readabilitymentioning

confidence: 99%

Lexical Complexity Prediction: An Overview

2023

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The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modelling has been applied to identify complex words in texts and substitute them for simpler alternatives. In this paper, we present an overview of computational approaches to lexical complexity prediction focusing on the work carried out on English data. We survey relevant approaches to this problem which include traditional machine learning classifiers (e.g. SVMs, logistic regression) and deep neural networks as well as a variety of features, such as those inspired by literature in psycholinguistics as well as word frequency, word length, and many others. Furthermore, we introduce readers to past competitions and available datasets created on this topic. Finally, we include brief sections on applications of lexical complexity prediction, such as readability and text simplification, together with related studies on languages other than English.

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“…This study depends on existing resources and generative models; thus, it is not free of biases and possible ethical considerations. One problem is the generation of text summaries that contain nonfactual information, meaning distortion, social biases such as political stances, or abusive language (Gooding, 2022). To mitigate these problems we plan to condition the generation of trained models for unsafe content or other harmful text to return an empty string.…”

Section: Ethical Considerationsmentioning

confidence: 99%

Analyzing Multi-Task-Learning for Abstractive Text Summarization

Kirstein¹,

Wahle²,

Gipp³

et al. 2023

Preprint

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Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of task grouping during the pre-finetuning stage to learn common skills, such as reading comprehension. To close this gap, we analyze the influence of multi-task learning strategies using task families for the English abstractive text summarization task. We group tasks into one of three strategies, i.e., sequential, simultaneous, and continual multi-task learning, and evaluate trained models through two downstream tasks. We find that certain combinations of task families (e.g., advanced reading comprehension and natural language inference) positively impact downstream performance. Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text summarization. Our code is publicly available 1 .• We evaluate the co-training of different task families using statistical (e.g., ROUGE) and semantic metrics (e.g., BERTScore) for 18 datasets.

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On the Ethical Considerations of Text Simplification

Cited by 10 publications

References 27 publications

Digital Comprehensibility Assessment of Simplified Texts among Persons with Intellectual Disabilities

Digital Comprehensibility Assessment of Simplified Texts among Persons with Intellectual Disabilities

Lexical Complexity Prediction: An Overview

Analyzing Multi-Task-Learning for Abstractive Text Summarization

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