An AI-Resilient Text Rendering Technique for Reading and Skimming Documents

TL;DR

This paper introduces GP-TSM, a novel text rendering technique that enhances reading and skimming by de-emphasizing less important information through saliency modulation, improving comprehension and user preference.

Contribution

The paper presents a new recursive sentence compression method for identifying text importance and a saliency modulation rendering technique that preserves grammatical structure.

Findings

Participants preferred GP-TSM over existing methods.

GP-TSM improved efficiency in answering comprehension questions.

The method maintains grammatical correctness while de-emphasizing less important text.

Abstract

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.