Natural language understanding is fundamental to knowledge acquisition in today’s information society. However, natural language is often ambiguous with frequent occurrences of complex terms, acronyms, and abbreviations that require substitution and disambiguation, for example, by “translation” from complex to simpler text for better understanding. These tasks are usually difficult for people with limited reading skills, second language learners, and non-native speakers. Hence, the development of text simplification systems that are capable of simplifying complex text is of paramount importance. Thus, we conducted a user study to identify which components are essential in a text simplification system. Based on our findings, we proposed an improved text simplification framework, covering a broader range of aspects related to lexical simplification — from complexity identification to lexical substitution and disambiguation — while supplementing the simplified outputs with additional information for better understandability. Based on the improved framework, we developed TextSimplifier, a modularised, context-sensitive, end-to-end simplification framework, and engineered its web implementation. This system targets lexical simplification that identifies complex terms and acronyms followed by their simplification through substitution and disambiguation for better understanding of complex language.