24 citations · 45 across the 7 of their papers we have counts for
7 papers
Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation
Aleksandar Savkov, Francesco Moramarco, Alex Papadopoulos Korfiatis +3
Evaluating automatically generated text is generally hard due to the inherently subjective nature of many aspects of the output quality. This difficulty is compounded in automatic…
User-Driven Research of Medical Note Generation Software
Tom Knoll, Francesco Moramarco, Alex Papadopoulos Korfiatis +7
A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there…
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation
Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera +5
In recent years, machine learning models have rapidly become better at generating clinical consultation notes; yet, there is little work on how to properly evaluate the generated c…
PriMock57: A Dataset Of Primary Care Mock Consultations
Alex Papadopoulos Korfiatis, Francesco Moramarco, Radmila Sarac +1
Recent advances in Automatic Speech Recognition (ASR) have made it possible to reliably produce automatic transcripts of clinician-patient conversations. However, access to clinica…
Towards objectively evaluating the quality of generated medical summaries
Francesco Moramarco, Damir Juric, Aleksandar Savkov +1
We 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…
A preliminary study on evaluating Consultation Notes with Post-Editing
Francesco Moramarco, Alex Papadopoulos Korfiatis, Aleksandar Savkov +1
Automatic summarisation has the potential to aid physicians in streamlining clerical tasks such as note taking. But it is notoriously difficult to evaluate these systems and demons…