# Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring

arXiv: https://arxiv.org/abs/2008.01809 · HTML: https://mlsanity.com/paper/2008.01809/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays](https://mlsanity.com/paper/2102.00781/) (2021)
- [eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback](https://mlsanity.com/paper/2501.00715/) (2025)
- ["Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays](https://mlsanity.com/paper/2103.04518/) (2021)

## Abstract (arXiv)

While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.