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

Abacha, Asma Ben; Mrabet, Yassine; Zhang, Yuhao; Shivade, Chaitanya; Langlotz, Curtis P.; Demner‐Fushman, Dina

doi:10.18653/v1/2021.bionlp-1.8

Cited by 56 publications

(31 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biomedical natural language processing (BioNLP) is an established field in computational linguistics, with a rich set of shared tasks including BioCreative and the competitions organized by the BioNLP workshop series (bio, 2021;Ben Abacha et al, 2021). Research topics include automatic information extraction from clinical reports, discharge summaries or life science articles, e.g., in the form of entity recognition for diseases, proteins, drug and gene names (Habibi et al, 2017;Giorgi and Bader, 2018;Lee et al, 2019, i.a.).…”

Section: Related Workmentioning

confidence: 99%

Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)

Wührl¹,

Klinger²

2022

Preprint

View full text Add to dashboard Cite

For a long time, text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experiences or patient-doctor interactions is sometimes limited. Information provided on social media, e.g., by patients and their relatives, complements the knowledge available in scientific text. It reflects the patient's journey and their subjective perspective on the process of developing symptoms, being diagnosed and offered a treatment, being cured or learning to live with a medical condition. The value of this type of data is therefore twofold: Firstly, it offers direct access to people's perspectives. Secondly, it might cover information that is not available elsewhere, including self-treatment or self-diagnoses. Named entity recognition and relation extraction are methods to structure information that is available in unstructured text. However, existing medical social media corpora focused on a comparably small set of entities and relations and were focused on particular domains, rather than putting the patient into the center of analyses. With this paper we contribute a corpus with a rich set of annotation layers following the motivation to uncover and model patients' journeys and experiences in more detail. We label 14 entity classes (incl. environmental factors, diagnostics, biochemical processes, patients' quality-of-life descriptions, pathogens, medical conditions, and treatments) and 20 relation classes (e.g., prevents, influences, interactions, causes) most of which have not been considered before for social media data. The publicly available dataset consists of 2,100 tweets with ≈6,000 entity and ≈3,000 relation annotations. In a corpus analysis we find that over 80 % of documents contain relevant entities. Over 50 % of tweets express relations which we consider essential for uncovering patients' narratives about their journeys.

show abstract

Section: Related Workmentioning

confidence: 99%

Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)

Wührl¹,

Klinger²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Pre-trained language models (PLMs) have been widely used in computer visions, natural language processing, etc., to effectively capture the linguistic information and knowledge inherited in natural languages. In this paper, we mainly discuss pre-trained language in NLP tokens 2 . One can read the review paper of PLMs in [190] for more details.…”

Section: Background: Pre-trained Language Modelsmentioning

confidence: 99%

“…The MEDIQA 2019 aims to explore the method development on the natural language inference (NLI), recognizing question entailment (RQE), and question answering (QA) in the medical domain. In bioNLP 2021, the MEDIQA 2021 [2] shared tasks were organized to address three tasks related to the summarization of medical documents, including the question summarization task, the multi-answer summarization task, and the radiology report summarization task.…”

Section: Competition Venuesmentioning

confidence: 99%

Pre-trained Language Models in Biomedical Domain: A Systematic Survey

Wang¹,

Xie²,

Pei³

et al. 2021

Preprint

View full text Add to dashboard Cite

Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. This also benefits biomedical domain: researchers from informatics, medicine, and computer science (CS) communities proposes various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks. However, the cross-discipline characteristics of biomedical PLMs hinder their spreading among communities; some existing works are isolated from each other without comprehensive comparison and discussions. It expects a survey that not only systematically reviews recent advances of biomedical PLMs and their applications but also standardizes terminology and benchmarks. In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks. Particularly, we discuss the motivations and propose a taxonomy of existing biomedical PLMs. Their applications in biomedical downstream tasks are exhaustively discussed. At last, we illustrate various limitations and future trends, which we hope can provide inspiration for the future research of the research community.CCS Concepts: • Computing methodologies → Natural language processing; Natural language generation; Neural networks; Bio-inspired approaches.

show abstract

“…Recently, Ben Abacha and Demner-Fushman (2019) defined the CHQ summarization task and introduced a new benchmark (MEQSUM) and a pointer-generator model. Ben Abacha et al (2021) organized the MEDIQA-21 shared task challenge on CHQ, multi-document answers, and radiology report summarization. Most of the participating team (Yadav et al, 2021b;He et al, 2021;Sänger et al, 2021) utilized transfer learning, knowledgebased, and ensemble methods to solve the question summarization task.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

Yadav¹,

Gupta²,

Abacha³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

Self Cite

View full text Add to dashboard Cite

The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary. We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-theart models. The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries.

show abstract

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

Cited by 56 publications

References 34 publications

Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)

Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)

Pre-trained Language Models in Biomedical Domain: A Systematic Survey

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

Contact Info

Product

Resources

About