Readers find text difficult to consume for many reasons. Summarization can address some of these difficulties, but introduce others, such as omitting, misrepresenting, or hallucinating information, which can be hard for a reader to notice. One approach to addressing this problem is to instead modify how the original text is rendered to make important information more salient. We introduce Grammar-Preserving Text Saliency Modulation (GP-TSM), a text rendering method with a novel means of identifying what to de-emphasize. Specifically, GP-TSM uses a recursive sentence compression method to identify successive levels of detail beyond the core meaning of a passage, which are de-emphasized by rendering words in successively lighter but still legible gray text. In a lab study (n=18), participants preferred GP-TSM over pre-existing word-level text rendering methods and were able to answer GRE reading comprehension questions more efficiently.

翻译：摘要：读者由于多种原因常感到文本阅读困难。文本摘要虽可缓解部分问题，却会引入信息遗漏、歪曲或幻觉等新问题，且这类问题往往不易被读者察觉。针对这一难题，可采取替代方案——通过修改原始文本的呈现方式来凸显重要信息。我们提出语法保持型文本显著性调制技术（GP-TSM），该文本渲染方法采用创新机制识别需弱化的内容。具体而言，GP-TSM通过递归式句子压缩方法，逐层识别超越段落核心含义的细节层级，将这些层级以灰度渐浅但仍清晰可辨的字体呈现以降低其显著性。实验室研究（n=18）表明，受试者更青睐GP-TSM相较于现有词级文本渲染方法的表现，并能在GRE阅读理解测试中更高效作答。