Fill in the BLANC: Human-free quality estimation of document summaries

Oleg Vasilyev, Vedant Dharnidharka, John Bohannon


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.
Anthology ID:
2020.eval4nlp-1.2
Volume:
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems
Month:
November
Year:
2020
Address:
Online
Editors:
Steffen Eger, Yang Gao, Maxime Peyrard, Wei Zhao, Eduard Hovy
Venue:
Eval4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–20
Language:
URL:
https://aclanthology.org/2020.eval4nlp-1.2
DOI:
10.18653/v1/2020.eval4nlp-1.2
Bibkey:
Cite (ACL):
Oleg Vasilyev, Vedant Dharnidharka, and John Bohannon. 2020. Fill in the BLANC: Human-free quality estimation of document summaries. In Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, pages 11–20, Online. Association for Computational Linguistics.
Cite (Informal):
Fill in the BLANC: Human-free quality estimation of document summaries (Vasilyev et al., Eval4NLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.eval4nlp-1.2.pdf
Video:
 https://slideslive.com/38939714