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Found 18 papers, 8 papers with code
Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain
The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world consumer health queries, (ii) a multi-answer summarization task that targeted aggregation of multiple relevant answers to a biomedical question into one concise and relevant answer, and (iii) a radiology report summarization task addressing the development of clinically relevant impressions from radiology report findings.
Evidence-based Fact-Checking of Health-related Claims
Our experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and open-domain claims.
3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology
The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged.
ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation
Here we present the Ambient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset to date tackling the problem of AI-assisted note generation from visit dialogue.
An Investigation of Evaluation Metrics for Automated Medical Note Generation
To study the correlation between the automatic metrics and manual judgments, we evaluate automatic notes/summaries by comparing the system and reference facts and computing the factual correctness, and the hallucination and omission rates for critical medical facts.
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards
The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.
Question-aware Transformer Models for Consumer Health Question Summarization
In this paper, we study the task of abstractive summarization for real-world consumer health questions.
Visual Question Generation from Radiology Images
Visual Question Generation (VQG), the task of generating a question based on image contents, is an increasingly important area that combines natural language processing and computer vision.
Question-Driven Summarization of Answers to Consumer Health Questions
This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches.
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.
On the Summarization of Consumer Health Questions
In this paper, we study neural abstractive models for medical question summarization.
A Question-Entailment Approach to Question Answering
One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions.
A dataset of clinically generated visual questions and answers about radiology images
We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers.
NLM\_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering
Tested on cQA-B-2016 test data, our RQE system outperformed the best system of the 2016 challenge in all measures with 77. 47 MAP and 80. 57 Accuracy.
A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature
Readers usually rely on abstracts to identify relevant medical information from scientific articles.
Annotating Named Entities in Consumer Health Questions
We describe a corpus of consumer health questions annotated with named entities.
