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Found 13 papers, 8 papers with code
Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews
We provide an overview of the MSLR2022 shared task on multi-document summarization for literature reviews.
MSˆ2: Multi-Document Summarization of Medical Studies
In support of this goal, we release MSˆ2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20K summaries derived from the scientific literature.
Do Multi-Document Summarization Models Synthesize?
In this paper we ask: To what extent do modern multi-document summarization models implicitly perform this type of synthesis?
Entity Anchored ICD Coding
Medical coding is a complex task, requiring assignment of a subset of over 72, 000 ICD codes to a patient's notes.
MS2: Multi-Document Summarization of Medical Studies
In support of this goal, we release MS^2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20k summaries derived from the scientific literature.
Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations
Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction).
Evidence Inference 2.0: More Data, Better Models
Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions.
ERASER: A Benchmark to Evaluate Rationalized NLP Models
We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i. e., the degree to which provided rationales influenced the corresponding predictions).
Inferring Which Medical Treatments Work from Reports of Clinical Trials
In this paper, we present a new task and corpus for making this unstructured evidence actionable.
Events Beyond ACE: Curated Training for Events
We explore a human-driven approach to annotation, curated training (CT), in which annotation is framed as teaching the system by using interactive search to identify informative snippets of text to annotate, unlike traditional approaches which either annotate preselected text or use active learning.
