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Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning ...
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Abstract:
Evaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the informativeness of the assessed summary and require human-generated references for each test summary. In this work, we propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning. Specifically, we design a new metric which covers both linguistic qualities and semantic informativeness based on BERT. To learn the metric, for each summary, we construct different types of negative samples with respect to different aspects of the summary qualities, and train our model with a ranking loss. Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries. Furthermore, we show that our method is general and transferable across datasets. ... : Long Paper in EMNLP 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2010.01781
https://arxiv.org/abs/2010.01781
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Inverted Bilingual Topic Models for Lexicon Extraction from Non-parallel Data ...
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Learning Crosslingual Word Embeddings without Bilingual Corpora ...
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