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

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arxiv 2002.09836 v2 pith:ERUVQCRA submitted 2020-02-23 cs.CL

classification cs.CL
keywords summaryblancdocumentqualityestimationapproachfullyhuman-free
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis

    cs.CL 2024-12 reject novelty 6.0 of 10

    A new large corpus of Indian court cases and a legal LLaMA model report very high judgment-prediction accuracy, but the evaluation leaks the outcome from the input text.

  2. Survey on Abstractive Text Summarization: Dataset, Models, and Metrics

    cs.AI 2024-12 conditional novelty 2.0 of 10

    A survey of abstractive text summarization models, datasets, and metrics, accompanied by a small experimental run of public transformer checkpoints on short, long, and multi-document samples.

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