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Found 13 papers, 3 papers with code
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose.
Cross-linguistic Comparison of Linguistic Feature Encoding in BERT Models for Typologically Different Languages
Though recently there have been an increased interest in how pre-trained language models encode different linguistic features, there is still a lack of systematic comparison between languages with different morphology and syntax.
The patient is more dead than alive: exploring the current state of the multi-document summarisation of the biomedical literature
Although multi-document summarisation (MDS) of the biomedical literature is a highly valuable task that has recently attracted substantial interest, evaluation of the quality of biomedical summaries lacks consistency and transparency.
Generative Debunking of Climate Misinformation
Misinformation about climate change causes numerous negative impacts, necessitating corrective responses.
Revisiting subword tokenization: A case study on affixal negation in large language models
In this work, we measure the impact of affixal negation on modern English large language models (LLMs).
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
We analyze how automated summarization evaluation metrics correlate with lexical features of generated summaries, to other automated metrics including several we propose in this work, and to aspects of human-assessed summary quality.
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation
Negation is poorly captured by current language models, although the extent of this problem is not widely understood.
LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation
In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
ITTC @ TREC 2021 Clinical Trials Track
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021 Clinical Trials Track.
Impact of detecting clinical trial elements in exploration of COVID-19 literature
The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature.
COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research
We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest.
