Multi-document summarization via deep learning techniques: A survey

C Ma, WE Zhang, M Guo, H Wang, QZ Sheng - ACM Computing Surveys, 2022 - dl.acm.org
Multi-document summarization (MDS) is an effective tool for information aggregation that
generates an informative and concise summary from a cluster of topic-related documents …

A survey on dialogue summarization: Recent advances and new frontiers

X Feng, X Feng, B Qin - arXiv preprint arXiv:2107.03175, 2021 - arxiv.org
Dialogue summarization aims to condense the original dialogue into a shorter version
covering salient information, which is a crucial way to reduce dialogue data overload …

[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence

A Holzinger, M Dehmer, F Emmert-Streib, R Cucchiara… - Information …, 2022 - Elsevier
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …

DERA: enhancing large language model completions with dialog-enabled resolving agents

V Nair, E Schumacher, G Tso, A Kannan - arXiv preprint arXiv:2303.17071, 2023 - arxiv.org
Large language models (LLMs) have emerged as valuable tools for many natural language
understanding tasks. In safety-critical applications such as healthcare, the utility of these …

The digital scribe in clinical practice: a scoping review and research agenda

MM van Buchem, H Boosman, MP Bauer, IMJ Kant… - NPJ digital …, 2021 - nature.com
The number of clinician burnouts is increasing and has been linked to a high administrative
burden. Automatic speech recognition (ASR) and natural language processing (NLP) …

Human evaluation and correlation with automatic metrics in consultation note generation

F Moramarco, AP Korfiatis, M Perera, D Juric… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …

Wanglab at mediqa-chat 2023: Clinical note generation from doctor-patient conversations using large language models

J Giorgi, A Toma, R Xie, SS Chen, KR An… - arXiv preprint arXiv …, 2023 - arxiv.org
This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic
clinical note generation from doctor-patient conversations. We report results for two …

[PDF][PDF] Overview of the MEDIQA-Sum Task at ImageCLEF 2023: Summarization and Classification of Doctor-Patient Conversations.

W Yim, AB Abacha, G Adams, N Snider… - CLEF (Working …, 2023 - ceur-ws.org
This paper presents the overview of the MEDIQA-Sum task at ImageCLEF 2023. MEDIQA-
Sum 2023 includes three subtasks, in which a doctor-patient dialogue source is given, and …

An investigation of evaluation methods in automatic medical note generation

AB Abacha, W Yim, G Michalopoulos… - Findings of the …, 2023 - aclanthology.org
Recent studies on automatic note generation have shown that doctors can save significant
amounts of time when using automatic clinical note generation (Knoll et al., 2022) …

PriMock57: A dataset of primary care mock consultations

AP Korfiatis, F Moramarco, R Sarac… - arXiv preprint arXiv …, 2022 - arxiv.org
Recent advances in Automatic Speech Recognition (ASR) have made it possible to reliably
produce automatic transcripts of clinician-patient conversations. However, access to clinical …