1 code implementation • sdp (COLING) 2022 • Lucy Lu Wang, Jay DeYoung, Byron Wallace
We provide an overview of the MSLR2022 shared task on multi-document summarization for literature reviews.
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.
no code implementations • 31 Jan 2023 • Jay DeYoung, Stephanie C. Martinez, Iain J. Marshall, Byron C. Wallace
In this paper we ask: To what extent do modern multi-document summarization models implicitly perform this type of synthesis?
no code implementations • 15 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.
2 code implementations • 13 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.
no code implementations • 7 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).
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.
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).
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.
no code implementations • 14 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.