| Challenge: | Existing citation recommendation systems rely on information of query documents such as author names and publication venue. |
| Approach: | They propose a content-based method for recommending citations in academic paper drafts . they embed a given query document into a vector space and use its nearest neighbors as candidates . |
| Outcome: | The proposed method outperforms published methods on PubMed and DBLP datasets without metadata. |
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| Challenge: | Existing citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. |
| Approach: | They propose a task of Recommending Missed Citations Identified by Reviewers to help improve citations of full papers. |
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Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications (2020.lrec-1)
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| Challenge: | Keeping up with the most recent scientific literature is a challenge for many researchers given the continuous and increasing growth of academic publications. |
| Approach: | They propose to use citation knowledge to provide personalised recommendations of recent scientific publications to a particular user by capturing authors’ publication history and enriched with different forms of paper citation. |
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ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation (2024.lrec-main)
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| Challenge: | Existing approaches for local citation recommendation map or translate a query to citation-worthy research papers. |
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Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study (2020.acl-main)
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| Challenge: | Existing studies on automatic generation of citation texts in scholarly papers have not investigated this problem. |
| Approach: | They propose to train an implicit citation extraction model based on BERT and a multi-source pointer-generator network with cross attention mechanism for citation text generation. |
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A High-Quality Gold Standard for Citation-based Tasks (L18-1)
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| Challenge: | Citation recommendation tasks involve recommending citations within their specific contexts. |
| Approach: | They propose to use arXiv.org's citation-dependent evaluation data set to evaluate citations . their data set is characterized by the fact that it exhibits almost zero noise in its extracted content . |
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CiteBART: Learning to Generate Citations for Local Citation Recommendation (2025.emnlp-main)
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| Challenge: | Local citation recommendation (LCR) suggests a set of papers for a citation placeholder in a given context. |
| Approach: | They introduce a citation-specific pre-training framework where author-date citation tokens are masked to learn to reconstruct them to fulfill local citation recommendation (LCR). |
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CiteBench: A Benchmark for Scientific Citation Text Generation (2023.emnlp-main)
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| Challenge: | Existing studies on citation text generation are based upon widely diverging task definitions, making it hard to study this task systematically. |
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BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation (2021.acl-long)
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| Challenge: | citing sentences capture salient information in cited papers and the connection between citing and citing papers. |
| Approach: | They propose a BAckground knowledge- and COntent-based framework for citing sentence generation that integrates two types of information: background knowledge and content. |
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Content-based Models of Quotation (2021.eacl-main)
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| Challenge: | Prior work has focused on manual feature engineering and development of frameworks to test factors that influence quotability. |
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Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference (2021.findings-acl)
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| Challenge: | Existing approaches to document-to-document similarity ranking are limited to relatively short documents or lack similarity labels. |
| Approach: | They propose a self-supervised method for document similarity ranking that can be applied to documents of arbitrary length. |
| Outcome: | The proposed model outperforms existing methods on large documents datasets. |