Search Results for author: Asma Ben Abacha

Found 14 papers, 7 papers with code

Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain

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.

Text Summarization

Evidence-based Fact-Checking of Health-related Claims

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.

Fact Checking Pretrained Language Models

Visual Question Generation from Radiology Images

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.

Computer Vision Image Augmentation +4

Question-Driven Summarization of Answers to Consumer Health Questions

1 code implementation18 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.

Question Answering

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

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.

Information Retrieval Natural Language Inference +1

A Question-Entailment Approach to Question Answering

3 code implementations23 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.

Information Retrieval Question Answering +1

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