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

About

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.

Oleg Vasilyev, Vedant Dharnidharka, John Bohannon• 2020

Related benchmarks

TaskDatasetResultRank
Factual Consistency EvaluationSummaC
CGS54.1
52
Factual Consistency EvaluationQAGS XSUM
Spearman Correlation1.6
39
Factual Consistency EvaluationQAGS CNNDM
Spearman Correlation22.2
38
Factual Consistency EvaluationTRUE benchmark
PAWS (AUC-ROC)56
37
Factual Consistency EvaluationSummEval
Spearman Correlation19
36
Opinion Summarization Metric EvaluationOPINSUMMEVAL
Aspect Relevance56
32
Factual Consistency EvaluationSamSum
Spearman Correlation9.1
30
Factual Consistency EvaluationFRANK CNNDM
Spearman Correlation34.2
30
Factual Consistency EvaluationFRANK-XSum (FRK-X)
Spearman Correlation6.5
30
Factual Consistency EvaluationFRK-C
Kendall's Tau26
22
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