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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Healthcare & Medicine and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — medical summarization
🐝 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