This paper presents a novel approach to assist students with dyslexia, ADHD, and short attention span in digesting any text-based information more efficiently. The proposed solution utilizes the Multilayer Perceptron (MLP) algorithm for complex text processing and summarization tasks. The tool leverages the T5 (Text-to-Text Transfer Transformer) model from Hugging Face, which treats every NLP task as a text generation task. The model is fine-tuned on specific tasks using a smaller dataset. The NLTK's Punkt Sentence Tokenizer is used to divide a text into a list of sentences. The application is served using Flask, a lightweight web server and framework. The tool also applies principles from Bionic Reading to enhance readability, which includes a bolding function and adjustments to line, word, and character spacing. The paper discusses the methodology, implementation, and results of the AI-based speed reading tool.

翻译：本文提出了一种新颖方法，旨在帮助患有阅读障碍、注意力缺陷多动症（ADHD）及注意力持续时间短的学生更高效地消化各类文本信息。该方案采用多层感知机（MLP）算法处理复杂文本的归纳总结任务。工具基于Hugging Face的T5（文本到文本迁移Transformer）模型，该模型将自然语言处理任务均视为文本生成任务，并针对特定任务使用小型数据集进行微调。利用自然语言工具包（NLTK）的Punkt句子分割器将文本划分为句子列表。应用程序基于轻量级Web服务器框架Flask提供服务。工具还融合了仿生阅读原则以增强可读性，具体包括加粗功能及对行距、词距、字距的调整。本文详细论述了该人工智能速读工具的方法论、实现过程及实验结果。