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WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers

Mario Sänger, Leon Weber, Ulf Leser
<span title="">2021</span> <i title="Association for Computational Linguistics"> Proceedings of the 20th Workshop on Biomedical Language Processing </i> &nbsp; <span class="release-stage">unpublished</span>
This paper describes our contribution for the MEDIQA-2021 Task 1 question summarization competition. We model the task as conditional generation problem.  ...  We used the resulting models as strong baselines and experimented with (i) integrating structured knowledge via entity embeddings, (ii) ensembling multiple generative models with the generator-discriminator  ...  Conclusion In this work we investigate the large-scale pretrained generative transformers PEGASUS and BART for the task of health-related consumer question summarization in the context of the MEDIQA 2021  ... 
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Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain

Asma Ben Abacha, Yassine Mrabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz, Dina Demner-Fushman
<span title="">2021</span> <i title="Association for Computational Linguistics"> Proceedings of the 20th Workshop on Biomedical Language Processing </i> &nbsp; <span class="release-stage">unpublished</span>
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  ...  We thank Anna Ripple (NLM/NIH) for her help with the manual annotation and Soumya Gayen (NLM/NIH) for his help with the summarization interface.  ... 
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