SentCite: a sentence-level citation recommender based on the salient similarity among multiple segments
Scientometrics, ISSN: 1588-2861, Vol: 127, Issue: 5, Page: 2521-2546
2022
- 3Citations
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Efficiently making adequate citations is becoming more challenging due to the rapidly increasing volume of publications. In practice, citing the appropriate references is a time-consuming and skill-required task. Accordingly, many studies have tried to help by providing citation-oriented support. In this field, citation recommendation is a significant research area because it addresses the problems of required profound skills and information overload. In this paper, we propose a sentence-level citation recommender, SentCite, that can identify the sentences that need links to references and can recommend citations. SentCite employs the convolutional recurrent neural network to extract the citing sentences and recommends citations based on the salient similarity between the sentences among the abstract, full text, and in-link context of the target papers. Unlike some other research in the big data domain, the recommended quality papers in this application are very limited. We proposed undersampling inlink context awareness to avoid overfitting problems. SentCite can recommend the most appropriate papers for the given sentences and outperforms other context-based methods in terms of improvement in mean reciprocal rank (MRR) 31.8%, mean average precision (MAP) 30.1%, and normalized discounted cumulative gain (NDCG) 33.8%.
Bibliographic Details
Springer Science and Business Media LLC
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