Search Results for author: Jay DeYoung

Found 15 papers, 9 papers with code

MSˆ2: Multi-Document Summarization of Medical Studies

1 code implementation EMNLP 2021 Jay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, Lucy Wang

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.

Document Summarization Multi-Document Summarization

Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations

1 code implementation23 May 2023 Lucy Lu Wang, Yulia Otmakhova, Jay DeYoung, Thinh Hung Truong, Bailey E. Kuehl, Erin Bransom, Byron C. Wallace

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.

Document Summarization Multi-Document Summarization

Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs

no code implementations5 May 2023 Somin Wadhwa, Jay DeYoung, Benjamin Nye, Silvio Amir, Byron C. Wallace

However, results from RCTs are presented in (often unstructured) natural language articles describing the design, execution, and outcomes of trials; clinicians must manually extract findings pertaining to interventions and outcomes of interest from such articles.

Entity Anchored ICD Coding

no code implementations15 Aug 2022 Jay DeYoung, Han-Chin Shing, Luyang Kong, Christopher Winestock, Chaitanya Shivade

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

2 code implementations13 Apr 2021 Jay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, Lucy Lu Wang

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.

Document Summarization Multi-Document Summarization

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations

no code implementations7 Oct 2020 Benjamin E. Nye, Jay DeYoung, Eric Lehman, Ani Nenkova, Iain J. Marshall, Byron C. Wallace

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).

Decision Making Relation Extraction

Evidence Inference 2.0: More Data, Better Models

1 code implementation WS 2020 Jay DeYoung, Eric Lehman, Ben Nye, Iain J. Marshall, Byron C. Wallace

Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions.

ERASER: A Benchmark to Evaluate Rationalized NLP Models

2 code implementations ACL 2020 Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace

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

2 code implementations NAACL 2019 Eric Lehman, Jay DeYoung, Regina Barzilay, Byron C. Wallace

In this paper, we present a new task and corpus for making this unstructured evidence actionable.

Events Beyond ACE: Curated Training for Events

no code implementations14 Sep 2018 Ryan Gabbard, Jay DeYoung, Marjorie Freedman

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.

Active Learning Event Argument Extraction

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