Search Results for author: Madeleine van Zuylen

Found 11 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

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

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

3 code implementations NAACL 2021 Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge.

Navigate

SciREX: A Challenge Dataset for Document-Level Information Extraction

1 code implementation ACL 2020 Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, Iz Beltagy

It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections.

Sentence

Fact or Fiction: Verifying Scientific Claims

2 code implementations EMNLP 2020 David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi

We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.

Claim Verification Domain Adaptation +1

Structural Scaffolds for Citation Intent Classification in Scientific Publications

1 code implementation NAACL 2019 Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field Cady

Identifying the intent of a citation in scientific papers (e. g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature.

Citation Intent Classification Classification +5

A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

1 code implementation NAACL 2018 Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz

In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline.

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