2021
EACL
EACL 2021
Towards Objectively Evaluating the Quality of Generated Medical Summaries
Abstract
AbstractWe propose a method for evaluating the quality of generated text by asking evaluators to count facts, and computing precision, recall, f-score, and accuracy from the raw counts. We believe this approach leads to a more objective and easier to reproduce evaluation. We apply this to the task of medical report summarisation, where measuring objective quality and accuracy is of paramount importance.
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Interdisciplinary Bridge
— Artificial Intelligence and Healthcare & Medicine and Machine Learning and Natural Language Processing
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Keyword Pioneer
— medical summarization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Core Methods > Representation Learning
Machine Learning > Application Areas > Domain Adaptation
Natural Language Processing > Applications > Summarization
Natural Language Processing > Applications > Text Generation
Healthcare & Medicine > Clinical > Medical AI