Structural Scaffolds for Citation Intent Classification in Scientific Publications

By Arman Cohan, Bridger Waleed Ammar, Madeleine van Zuylen, Field Cady. 2019

311 citations recorded by the source corpus.

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. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.

Subfields: Artificial Intelligence, Molecular Biology, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

SciBERT: A Pretrained Language Model for Scientific Text (2019, methodology): “In addition, SCIBERT achieves new SOTA results on ACLARC (Cohan et al., 2019), and the NER part of SciERC (Luan et al., 2018).”
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021, background): “…comprehension Lin et al. 2019 rotten_tomatoes cls/sentiment analysis Pang and Lee 2005 samsum cg/summarization Gliwa et al. 2019 scicite cls/other Cohan et al. 2019 sciq qa/multiple-choice qa Welbl et al. 2017 scitail cls/nli Khot et al. 2018 search_qa qa/closed-book qa Dunn et al. 2017 sick…”
S2ORC: The Semantic Scholar Open Research Corpus (2020, background): “Other tasks that leverage citation contexts include classifying citation intent (Teufel et al., 2006; Jurgens et al., 2018; Cohan et al., 2019), identifying citation sentiment (Athar and Teufel, 2012), identifying meaningful citations (Valen-zuela et al., 2015), extracting key phrases (Caragea et…”
TLDR: Extreme Summarization of Scientific Documents (2020, methodology): “Incorporating auxiliary scaffold tasks via multitask learning has been studied before for improving span-labeling and text classification (Swayamdipta et al., 2018b; Cohan et al., 2019).”
GORC: A large contextual citation graph of academic papers (2019, background): “…2015; Yu et al., 2012), in-line citation recommendation (Jeong et al., 2019; Huang et al., 2015; He et al., 2010), categorizing citation intent (Cohan et al., 2019; Teufel et al., 2006), paper summarization (Cohan and Goharian, 2015; Mitrovic and M¨uller, 2015; Qazvinian and Radev, 2008;…”
OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models (2021, background): “Besides pre-training for general purposes, more and more language models are targeting at specific domains, such as BioBERT [26] for biomedical field and SciBERT [2] for academic field, which establish new state-of-the-art on many domain-related benchmarks such as named entity recognition [10, 32], ”
SciBERT: Pretrained Contextualized Embeddings for Scientific Text (2019, unknown): “In addition, SCIBERT outperforms the SOTA on ScienceCite (Cohan et al., 2019).”
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (2019, methodology): “Given sentences from Cohan et al. (2019) which were originally designed for citation intent prediction, we run these sentences individually through our models.”
ScholarBERT: Bigger is Not Always Better (2022, background): “14M papers from Semantic Scholar (Cohan et al., 2019), 18% in CS, 82% in Bio Bio, CS 3.”
Control Prefixes for Text Generation (2021, methodology): “Auxiliary scaffold tasks Incorporating auxiliary scaffold tasks via multitask learning has been previously used for improving span-labeling and text classification (Swayamdipta et al., 2018; Cohan et al., 2019).”
Control Prefixes for Parameter-Efficient Text Generation (2021, methodology): “This strategy was inspired by previous work which incorporates auxiliary scaffold tasks in multitask learning to improve span-labeling and text classification (Swayamdipta et al., 2018; Cohan et al., 2019; Cachola et al., 2020).”
What to Pre-Train on? Efficient Intermediate Task Selection (2021, background): “96.4 95.60 SciCite (Cohan et al., 2019) 8.2K citation intents scientific papers acc.”