no code implementations • EMNLP (DeeLIO) 2020 • Travis Goodwin, Dina Demner-Fushman
Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e. g., world) knowledge.
no code implementations • BioNLP (ACL) 2022 • Deepak Gupta, Dina Demner-Fushman
The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling), and (ii) a visual answer localization task.
1 code implementation • Findings (EMNLP) 2021 • Mourad Sarrouti, Asma Ben Abacha, Yassine Mrabet, Dina Demner-Fushman
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.
no code implementations • NAACL (BioNLP) 2021 • Asma Ben Abacha, Yassine Mrabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz, Dina Demner-Fushman
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.
no code implementations • 10 Oct 2024 • Sarvesh Soni, Dina Demner-Fushman
Regular documentation of progress notes is one of the main contributors to clinician burden.
no code implementations • 21 Sep 2023 • Deepak Gupta, Kush Attal, Dina Demner-Fushman
Toward this, this paper is focused on answering health-related questions asked by the public by providing visual answers from medical videos.
no code implementations • 7 May 2023 • Deepak Gupta, Dina Demner-Fushman
Pre-trained language models (PLMs) have proven to be effective for document re-ranking task.
1 code implementation • 21 Oct 2022 • Kush Attal, Brian Ondov, Dina Demner-Fushman
Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature.
1 code implementation • 5 Oct 2022 • Deepak Gupta, Russell Loane, Soumya Gayen, Dina Demner-Fushman
We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets.
no code implementations • 28 Sep 2022 • Travis R. Goodwin, Dina Demner-Fushman
Clinical language processing has received a lot of attention in recent years, resulting in new models or methods for disease phenotyping, mortality prediction, and other tasks.
1 code implementation • 14 Jun 2022 • Shweta Yadav, Deepak Gupta, Dina Demner-Fushman
The quest for seeking health information has swamped the web with consumers' health-related questions.
2 code implementations • 30 Jan 2022 • Deepak Gupta, Kush Attal, Dina Demner-Fushman
This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions.
1 code implementation • ACL 2021 • Shweta Yadav, Deepak Gupta, Asma Ben Abacha, Dina Demner-Fushman
The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.
no code implementations • 1 Jun 2021 • Shweta Yadav, Deepak Gupta, Asma Ben Abacha, Dina Demner-Fushman
In this paper, we study the task of abstractive summarization for real-world consumer health questions.
no code implementations • 19 Apr 2021 • Kirk Roberts, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, Kyle Lo, Ian Soboroff, Ellen Voorhees, Lucy Lu Wang, William R Hersh
We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19.
no code implementations • COLING 2020 • Yassine Mrabet, Dina Demner-Fushman
Efficient document summarization requires evaluation measures that can not only rank a set of systems based on an average score, but also highlight which individual summary is better than another.
1 code implementation • COLING 2020 • Travis Goodwin, Max Savery, Dina Demner-Fushman
Recent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Travis Goodwin, Max Savery, Dina Demner-Fushman
Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents.
1 code implementation • WS 2020 • Mourad Sarrouti, Asma Ben Abacha, Dina Demner-Fushman
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.
1 code implementation • 18 May 2020 • Max Savery, Asma Ben Abacha, Soumya Gayen, Dina Demner-Fushman
This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches.
no code implementations • 9 May 2020 • Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, Lucy Lu Wang
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic.
no code implementations • 16 Oct 2019 • Travis R. Goodwin, Dina Demner-Fushman
In this paper we present OSCAR (Ontology-based Semantic Composition Augmented Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pretraining.
no code implementations • 13 Aug 2019 • Surabhi Datta, Yuqi Si, Laritza Rodriguez, Sonya E Shooshan, Dina Demner-Fushman, Kirk Roberts
We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL).
1 code implementation • WS 2019 • Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman
MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.
1 code implementation • ACL 2019 • Asma Ben Abacha, Dina Demner-Fushman
In this paper, we study neural abstractive models for medical question summarization.
3 code implementations • 23 Jan 2019 • Asma Ben Abacha, Dina Demner-Fushman
One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions.
no code implementations • Scientific Data 2018 • Jason J. Lau, Soumya Gayen, Asma Ben Abacha, Dina Demner-Fushman
We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers.
no code implementations • SEMEVAL 2017 • Asma Ben Abacha, Dina Demner-Fushman
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.
no code implementations • ACL 2017 • Yassine Mrabet, Halil Kilicoglu, Dina Demner-Fushman
Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment.
no code implementations • COLING 2016 • Suchet Chachra, Asma Ben Abacha, Sonya Shooshan, Laritza Rodriguez, Dina Demner-Fushman
Readers usually rely on abstracts to identify relevant medical information from scientific articles.
no code implementations • LREC 2016 • Kirk Roberts, Dina Demner-Fushman
From these, 468 specific questions are found containing 259 unique medical concepts and requiring 53 unique predicates to represent the logical forms.
no code implementations • LREC 2016 • Halil Kilicoglu, Asma Ben Abacha, Yassine Mrabet, Kirk Roberts, Laritza Rodriguez, Sonya Shooshan, Dina Demner-Fushman
We describe a corpus of consumer health questions annotated with named entities.
1 code implementation • CVPR 2016 • Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
no code implementations • LREC 2014 • Kirk Roberts, Kate Masterton, Marcelo Fiszman, Halil Kilicoglu, Dina Demner-Fushman
This paper presents a method for annotating question decomposition on complex medical questions.