Exploration of Summarization by Generative Language Models for Automated Scoring of Long Essays
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 26 Oct 2025.
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15 of 37 references.
References · 15
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- 2023Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessmentcited by 230
- 2019HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarizationcited by 121
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- 2022On Efficient Approximate Queries over Machine Learning Modelscited by 7
- 2022Quantized Training of Gradient Boosting Decision Treescited by 6
- 2023Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Modelscited by 2
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Citing papers per year: 2026: 2.
2 papers.