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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
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In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.archives-ouvertes.fr/hal-03466171 ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩ (2021)
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THEaiTRobot 1.0
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Rosa, Rudolf; Dušek, Ondřej; Kocmi, Tom. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2021. : The Švanda Theatre in Smíchov, 2021. : The Academy of Performing Arts in Prague, Theatre Faculty (DAMU), 2021
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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ...
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MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization ...
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Abstract:
One of the most challenging aspects of current single-document news summarization is that the summary often contains 'extrinsic hallucinations', i.e., facts that are not present in the source document, which are often derived via world knowledge. This causes summarization systems to act more like open-ended language models tending to hallucinate facts that are erroneous. In this paper, we mitigate this problem with the help of multiple supplementary resource documents assisting the task. We present a new dataset MiRANews and benchmark existing summarization models. In contrast to multi-document summarization, which addresses multiple events from several source documents, we still aim at generating a summary for a single document. We show via data analysis that it's not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiRANews are better grounded on assisting documents than in the main source articles. An error analysis of generated summaries from pretrained models ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
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URL: https://underline.io/lecture/39839-miranews-dataset-and-benchmarks-for-multi-resource-assisted-news-summarization https://dx.doi.org/10.48448/ef01-yt90
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One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech ...
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Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge ...
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RankME: Reliable Human Ratings for Natural Language Generation ...
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Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity ...
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The E2E Dataset: New Challenges For End-to-End Generation ...
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