Exploration of Summarization by Generative Language Models for Automated Scoring of Long Essays
arXiv:2510.22830
Abstract
BERT and its variants are extensively explored for automated scoring. However, a limit of 512 tokens for these encoder-based models showed the deficiency in automated scoring of long essays. Thus, this research explores generative language models for automated scoring of long essays via summarization and prompting. The results revealed great improvement of scoring accuracy with QWK increased from 0.822 to 0.8878 for the Learning Agency Lab Automated Essay Scoring 2.0 dataset.
19 pages, 5 Tables 7 Figures, Presentation at Artificial Intelligence in Measurement and Education Conference (AIME-Con)