Transfer Learning based Automated Essay Summarization
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Abstract
The human evaluation of essays has become a very time-consuming process as the number of schools and universities has grown. The available software entities are unable to assess the sentiment associated with essays. Thus, we propose a model using Natural Language Processing to assess the essay based on both grammar and sentiment associated with the essay by using linear regression and ULMFiT (Universal Language Model Fine-tuning for Text Classification) models. Evaluation of essay is done in two parts. Part one is on essay grading with respect to grammar with maximum 12 and minimum 0 grade points and in part two score of 0/1 for sentiment analysis with 0 being negative and 1 being positive. The model can be used to score the essay and discard any essay with a score less than a specified value or specified sentiment score.
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