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arXiv · 1905.13322

Assessing The Factual Accuracy of Generated Text

Ben Goodrich·Vinay Rao·Mohammad Saleh·Peter J Liu
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Abstract

We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. The end-to-end models are shown to be able to extract complete sets of facts from datasets with full pages of text. We then analyse multiple models that estimate factual accuracy on a Wikipedia text summarization task, and show their efficacy compared to ROUGE and other model-free variants by conducting a human evaluation study.

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Ben Goodrich, Vinay Rao, Mohammad Saleh, Peter J Liu. 2019-05-30. Assessing The Factual Accuracy of Generated Text. https://doi.org/10.1145/3292500.3330955

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