Structural Scaffolds for Citation Intent Classification in Scientific Publications (1904.01608v2)

Abstract: 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.

• The paper's main contribution is a neural multitask learning framework that uses structural scaffolds to achieve a 13.3% absolute F1 score increase on the ACL-ARC dataset.
• The methodology incorporates auxiliary tasks for predicting section titles and citation-worthiness, replacing manual feature engineering with contextualized learning.
• The introduction of the SciCite dataset demonstrates the approach's broad generalizability and potential for enhancing citation analysis across multiple domains.

Analysis of "Structural Scaffolds for Citation Intent Classification in Scientific Publications"

This paper presents a significant contribution to citation intent classification by introducing a neural multitask learning framework that incorporates structural scaffolds from scientific documents to improve the accuracy of citation intent prediction. The framework is evaluated on two datasets: the ACL-ARC dataset and the SciCite dataset, which the authors have introduced as a new contribution. Both datasets showcase the framework's superior performance over existing methodologies, particularly those reliant on manually engineered features and external linguistic resources.

Contributions and Methodology

The authors propose a model that leverages two auxiliary tasks—predicting the section title where the citation appears and determining whether a sentence warrants a citation. These auxiliary tasks, referred to as structural scaffolds, are inherently connected to the main task and enable the model to draw from naturally occurring labels within scientific texts, thereby circumventing the need for additional manual annotation. By incorporating these scaffolds, the proposed model observes an absolute 13.3% increase in F1 score over the previous state-of-the-art on the ACL-ARC dataset, achieving 67.9%.

Additionally, the authors introduce a new dataset, SciCite, which emphasizes citation intents across multiple scientific domains, addressing the limitations of domain-specific and smaller datasets. The SciCite dataset allows for a more general and robust evaluation of citation intent classification techniques and demonstrates the practical efficacy of the proposed model across diverse domains.

Discussion of Results

The results of the proposed model are compelling. On the ACL-ARC dataset, the model significantly outperforms existing benchmarks, demonstrating the value of integrating structurally relevant information from scientific papers. The use of ELMo embeddings further bolsters its performance, highlighting the importance of contextualized word representations in enhancing the model's understanding of citation intents.

The introduction of the SciCite dataset further presents a meaningful advancement. The authors report strong F1 scores on this dataset, confirming the generalizability of their approach beyond the ACL-ARC dataset and suggesting the potential for widespread application across various academic disciplines.

Implications and Future Directions

The implications of this research are twofold: first, they present a paradigm shift away from manually-intensive feature extraction towards more data-driven methodologies, facilitated by multitask learning frameworks. Second, they broaden the scope for automated analysis of scientific literature, potentially aiding in more nuanced assessments of scientific impact and improving information retrieval systems in academic databases.

Looking forward, there are several avenues for further exploration based on this work. The potential application of other contextualized embeddings such as BERT or domain-specific models like SciBERT could enhance the model's performance and extract deeper semantic features pertinent to citation classification. Additionally, the exploration of other auxiliary tasks related to document structures or the introduction of domain ontologies as additional scaffolding mechanisms could provide fruitful enhancements.

Overall, this paper provides a methodological advancement in the field of citation intent classification, offering insights and tools that might find substantial application across various AI-driven academic tools and platforms. The release of the SciCite dataset will further help facilitate research in this domain, encouraging more robust, generalizable solutions to understanding scientific discourse.