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Research Scholar, PG and Research Department of Computer Science, Government Arts and Science College , Tiruppur , India
Assistant Professor, PG and Research Department of Computer Science, Government Arts and Science College , Tiruppur , India
Blind students usually are subjected to a substantial impediment of reading and accessing electronic documents, especially data that are noisy and those that are carefully designed. Traditional NLP models severely underestimate or misinterpret mathematical expressions in which symbols are represented as notation. It is a critical problem in the educational field, accessibility, and report generation programs, where in-depth knowledge of mathematical content is a priority. State-of-the-art document summarisation systems tend to fail in noisy text, disordered document structures, and non-textual content, e.g., equations, images, and charts. This paper introduces a powerful preprocessing model that focuses on improving input quality, semantic coherence, and readability. The process consists of sophisticated text cleaning, discerning structuring, and an extensive content interpretation model. The paper presents a proposal to simplify and verbalise mathematical expressions using a rule-based, context-sensitive language called the Verbalizer Rule (VR). The system translates complex mathematical syntax into human-readable natural-language descriptions by pattern-matching expressions and translating semantic meaning using clues in the context. Experiments demonstrate that this method achieves much higher readability scores and summarisation quality than state-of-the-art models. In the assessment, the Proposed CARMEN model, using the ROUGE metrics 1, 2, and L, yields a ROUGE score of more than 0.8333 among the other verbalizers.
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