Papers by Madeleine van Zuylen

8 papers
Construction of the Literature Graph in Semantic Scholar (N18-3)

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Challenge: Fig. 1 summarizes a scalable system for organizing published scientific literature into a heterogeneous graph . authors describe methods used to enable semantic features in www.semanticscholar.org .
Approach: They describe a scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
Outcome: The proposed system can be deployed on a scalable platform and report empirical results for each task.
SciREX: A Challenge Dataset for Document-Level Information Extraction (2020.acl-main)

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Challenge: Conventional datasets and methods for information extraction focus on within-sentence relations from general Newswire text.
Approach: They propose a document-level IE dataset that integrates automatic and human annotations to annotate entities and document- level N-ary relation identification from scientific articles.
Outcome: The proposed dataset extends state-of-the-art IE models to document-level IE.
MedICaT: A Dataset of Medical Images, Captions, and Textual References (2020.findings-emnlp)

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Challenge: Existing largescale datasets explicitly exclude compound figures . existing systems lack this ability to identify relevant subfigures .
Approach: They propose a dataset of medical images in context that allows figure-to-text alignment . they use captions, inline references and manually annotated subfigures for compound figures .
Outcome: The proposed dataset demonstrates the utility of inline references in image-text matching.
MSˆ2: Multi-Document Summarization of Medical Studies (2021.emnlp-main)

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Challenge: Existing datasets for multi-document summarization (MDS) are either in the general domain, such as WikiSum, or very small such as DUC 1 or TAC 2011 . Existing systems for summarizing biomedical literature take 1-2 years to complete .
Approach: They propose to use a multi-document summarization system based on BART to assess the quality of the summarized biomedical literature.
Outcome: The proposed system has high summarization quality, but significant work remains to achieve it.
Structural Scaffolds for Citation Intent Classification in Scientific Publications (N19-1)

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Challenge: Existing methods for identifying intent of citations are limited by external linguistic resources and hand-engineered features.
Approach: They propose a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents.
Outcome: The proposed model achieves a 13.3% increase in F1 score on an existing ACL anthology dataset without external linguistic resources or hand-engineered features as done in existing methods.
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications (N18-1)

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Challenge: a dataset of 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR is presented to study peer reviews.
Approach: They propose to use the dataset to collect peer reviews from top-tier venues including ACL, NIPS and ICLR and to use it to create a dataset of peer reviews for research purposes.
Outcome: The proposed dataset includes 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR.
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)

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Challenge: COVID-19 has spawned a diverse body of scientific literature that is challenging to navigate . researchers are using automated tools to help find useful knowledge .
Approach: They develop a schema to extract mechanism relations from scientific papers . their search engine, dataset and code are publicly available .
Outcome: The proposed schema outperforms PubMed search in clinical trials.
Fact or Fiction: Verifying Scientific Claims (2020.emnlp-main)

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Challenge: SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales.
Approach: They construct a dataset of 1.4K scientific claims paired with evidence-containing abstracts annotated with labels and rationales to test their system.
Outcome: The proposed system can verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus.

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