MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
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Abstract (arXiv)
A robust evaluation metric has a profound impact on the development of text generation systems. A desirable metric compares system output against references based on their semantics rather than surface forms. In this paper we investigate strategies to encode system and reference texts to devise a metric that shows a high correlation with human judgment of text quality. We validate our new metric, namely MoverScore, on a number of text generation tasks including summarization, machine translation, image captioning, and data-to-text generation, where the outputs are produced by a variety of neural and non-neural systems. Our findings suggest that metrics combining contextualized representations with a distance measure perform the best. Such metrics also demonstrate strong generalization capability across tasks. For ease-of-use we make our metrics available as web service.
Key facts. Cited by 418 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Sep 2019.
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Citing papers per year: 2021: 57; 2022: 101; 2023: 87; 2024: 84; 2025: 47; 2026: 42.
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