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 • 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.
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 • 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.