基于机器阅读理解的论文 辅助阅读系统构建.

In the era of informatization and digitization, the rapid increase in the number of scientific papers has given rise to various challenges, such as lengthy articles, difficulty in information extraction and high time costs associated with reading. Literature reading challenges for researchers are increasingly tedious and time-consuming. By utilizing the language models, the assited reading system of scientific papers has been designed to address these challenges. By adopting machine reading comprehension technology as the core, the system parses scientific texts and offers some common questions to achieve automated response capabilities. By fully utilizing the pre-trained language model PERT, the system enhances its capabilities in semantic understanding and information extraction, effectively resolving various challenges in reading scientific papers and helping readers improve the efficiency of scientific literature review. [ABSTRACT FROM AUTHOR]