@inproceedings{vasilyev-etal-2020-fill,
title = "Fill in the {BLANC}: Human-free quality estimation of document summaries",
author = "Vasilyev, Oleg and
Dharnidharka, Vedant and
Bohannon, John",
editor = "Eger, Steffen and
Gao, Yang and
Peyrard, Maxime and
Zhao, Wei and
Hovy, Eduard",
booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.eval4nlp-1.2",
doi = "10.18653/v1/2020.eval4nlp-1.2",
pages = "11--20",
abstract = "We present BLANC, a new approach to the automatic estimation of document summary quality. Our goal is to measure the functional performance of a summary with an objective, reproducible, and fully automated method. Our approach achieves this by measuring the performance boost gained by a pre-trained language model with access to a document summary while carrying out its language understanding task on the document{'}s text. We present evidence that BLANC scores have as good correlation with human evaluations as do the ROUGE family of summary quality measurements. And unlike ROUGE, the BLANC method does not require human-written reference summaries, allowing for fully human-free summary quality estimation.",
}
Markdown (Informal)
[Fill in the BLANC: Human-free quality estimation of document summaries](https://aclanthology.org/2020.eval4nlp-1.2) (Vasilyev et al., Eval4NLP 2020)
ACL