Paraphrase Generation and Identification at Paragraph-Level

The availability and growth of tools and natural language generation (NLG) models that are used to paraphrase text could be helping to improve students’ writing and comprehension skills or a threat to intellectual property and educational integrity specifically when the text has been copied from other authors. These tools can be used by plagiarists to paraphrase individual words, phrases, sentences, and paragraphs. To solve this issue, much work has been done on plagiarism detection (PD) and paraphrase identification (PI) utilising downstream tasks and natural language processing (NLP) methods. These works mainly focus on sentence length and sentence-level paraphrasing. In this paper, we investigate paragraph-length and paragraph-level paraphrasing as the most common method of committing plagiarism is copying and paraphrasing paragraphs from other authors. Here, we construct a novel, large-scale paragraph-level paraphrasing dataset by implementing and examining a state-of-the-art Transformer-based model to reorder and paraphrase sentences without affecting a paragraph's meaning. In a first-of-a-kind study, we consider both intra-sentence and inter-sentence similarity before examining the efficiency of state-of-the-art Transformer-based models in detecting paraphrased paragraphs. We offer a technique that serves as both a tool for honing paraphrasing skills and a means of identifying plagiarism. Our outcomes surpass those presented in the existing literature.