24 citations · 63 across the 10 of their papers we have counts for
10 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 more patient friendly clinical notes through language models and ontologies
Francesco Moramarco, Damir Juric, Aleksandar Savkov +8
Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients…
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…
Correlations between Word Vector Sets
Vitalii Zhelezniak, April Shen, Daniel Busbridge +2
Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic text…
Correlation Coefficients and Semantic Textual Similarity
Vitalii Zhelezniak, Aleksandar Savkov, April Shen +1
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals…
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Vitalii Zhelezniak, Aleksandar Savkov, April Shen +3
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, wh…