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MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting ...
Abstract: Citation context analysis (CCA) is an important task in natural language processing that studies how and why scholars discuss each others' work. Despite decades of study, traditional frameworks for CCA have largely relied on overly-simplistic assumptions of how authors cite, which ignore several important phenomena. For instance, scholarly papers often contain rich discussions of cited work that span multiple sentences and express multiple intents concurrently. Yet, CCA is typically approached as a single-sentence, single-label classification task, and thus existing datasets fail to capture this interesting discourse. In our work, we address this research gap by proposing a novel framework for CCA as a document-level context extraction and labeling task. We release MultiCite, a new dataset of 12,653 citation contexts from over 1,200 computational linguistics papers. Not only is it the largest collection of expert-annotated citation contexts to-date, MultiCite contains multi-sentence, multi-label citation ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2107.00414
https://dx.doi.org/10.48550/arxiv.2107.00414
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2
CDLM: Cross-Document Language Modeling ...
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3
ParsiNLU: A Suite of Language Understanding Challenges for Persian ...
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4
Enriching Scientific Paper Embeddings with Citation Context
Henner, Kevin. - 2019
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5
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions ...
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6
Triaging Content Severity in Online Mental Health Forums ...
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7
Revisiting Summarization Evaluation for Scientific Articles ...
Cohan, Arman; Goharian, Nazli. - : arXiv, 2016
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