Audience-Centric Text Simplification: Navigating Potential Errors and Misinterpretations

In this article, we explore the challenges of text simplification and how it can impact readers’ understanding of complex concepts. While simplification systems aim to improve readability by removing complexity, they can also introduce errors or oversimplify content, leading to inaccurate interpretations. To address these issues, recent research has focused on developing techniques to control the degree of simplicity in output, tailoring it to specific audiences and reading levels.
The article begins by acknowledging that text simplification is highly audience-centric and that what makes a text simple to read depends on the reader’s literacy skills and preferences. The authors explain that simplifying complex terms by replacing them with simpler synonyms can help non-native speakers, but this approach may not be suitable for all audiences or contexts. They also discuss how restructuring text into shorter sentences can improve readability, but this can lead to a loss of critical nuances and potential misconceptions.
To address these challenges, the authors introduce low-level control tokens that allow users to specify the nature of simplification operations, such as the word length ratio or maximum dependency tree depth. They demonstrate how these control values can be used to obtain distinct simplifications of the same input for different audiences or reading levels.
The article also discusses previous research on text simplification and its limitations, highlighting the need for more advanced techniques that can balance simplicity with accuracy. The authors conclude by emphasizing the importance of developing evaluation metrics that can accurately assess the quality of simplified texts and ensure their readability and accuracy for different audiences and contexts.
In summary, this article provides a comprehensive overview of the challenges and limitations of text simplification, highlighting the need for more sophisticated techniques that can balance simplicity with accuracy. By using low-level control tokens to tailor the degree of simplicity in output, researchers are working towards developing more effective and accurate text simplification systems.