Realistic Citation Count Prediction Task for Newly Published Papers (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies on citation count prediction assume that future citation counts of academic papers have not had enough time pass since publication. |
| Approach: | They propose to use citation counts of newly published papers as a realistic citation count prediction task and to use them to leverage the citations of papers shortly after publication. |
| Outcome: | The proposed methods significantly improve the performance of citation count prediction for newly published papers in a realistic setting. |
Similar Papers
Is Peer-Reviewing Worth the Effort? (2025.coling-main)
Copied to clipboard
| Challenge: | Using early returns and venue, we can predict which papers will be highly cited in the future. |
| Approach: | They ask whether early returns are predictive of papers' citations . |
| Outcome: | The authors show early returns are more predictive than venue . early returns also predicts which papers will be highly cited in the future . |
Comparing Edge-based and Node-based Methods on a Citation Prediction Task (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Citation Prediction is the task of estimating whether paper a cites paper b. |
| Approach: | They propose a new Citation Prediction task that evaluates both a node-based model and an edge-based one to quantify these trends. |
| Outcome: | The proposed model improves with larger training sets and degrades with longer forecast horizons. |
A High-Quality Gold Standard for Citation-based Tasks (L18-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed data set exhibits almost zero noise in extracted content and all citations are linked to their correct publications. |
Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study (2020.acl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model can generate short texts to describe cited papers in scholarly papers with training data. |
Predicting Long-Term Citations from Short-Term Linguistic Influence (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to quantify linguistic influence in timestamped documents are not informative about extent to which a paper affected subsequent publications. |
| Approach: | They propose to quantify linguistic influence in timestamped document collections by estimating a Hawkes process with a low-rank parameter matrix and identify lexical and semantic changes using contextual embeddings and word frequencies. |
| Outcome: | The proposed method is based on an online evaluation with incremental temporal training/test splits, in comparison with a strong baseline that includes predictors for initial citation counts, topics, and lexical features. |
Related Work and Citation Text Generation: A Survey (2024.emnlp-main)
Copied to clipboard
| Challenge: | Academic research paper authors must perform literature review to compare work with prior work . authors must compose coherent story that connects prior work and current work based on author's understanding of field . |
| Approach: | They propose to use automatic related work generation (RWG) to generate papers . authors summarize key approaches and define tasks in a zoo of historical works . |
| Outcome: | a new study summarises key approaches and defines the tasks and discusses the challenges of RWG. |
A Neural Citation Count Prediction Model based on Peer Review Text (D19-1)
Copied to clipboard
| Challenge: | Recent studies have focused on extracting or mining useful features from the paper itself or the associated authors. |
| Approach: | They propose to utilize peer review data for the CCP task with a neural prediction model to learn a comprehensive semantic representation for peer review text. |
| Outcome: | The proposed model improves on the peer review data and hand-crafted features. |
Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines (2024.lrec-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework outperforms existing methods in all metrics and will motivate future research on this challenging task. |
Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications (2020.lrec-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed dataset captures authors’ publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, cited contexts, and citation types. |
CiteBench: A Benchmark for Scientific Citation Text Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on citation text generation are based upon widely diverging task definitions, making it hard to study this task systematically. |
| Approach: | They propose a benchmark for citation text generation that unifies multiple datasets and enables standardized evaluation of citation texts across task designs and domains. |
| Outcome: | The proposed benchmark examines the performance of multiple strong baselines and enables standardized evaluation of citation text generation models across task designs and domains. |