| 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. |
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DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions (P19-1)
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| Challenge: | Existing peer review system is not straightforward and requires domain knowledge, expertise, and intelligence of human reviewers, which is somewhat elusive with the current state of AI. |
| Approach: | They propose to use peer review texts to predict acceptance or rejection of a manuscript based on reviewer sentiment. |
| Outcome: | The proposed deep neural architecture achieves significant performance improvement over baselines (29% error reduction) in a recently released dataset of peer reviews. |
Autonomous Machine Learning-Based Peer Reviewer Selection System (2025.coling-demos)
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| Challenge: | Existing systems that match papers with experts are inefficient and often require long turnaround times. |
| Approach: | They propose an autonomous peer reviewer selection system that employs the natural language processing model to match submitted papers with expert reviewers independently of traditional journals and conferences. |
| Outcome: | The proposed system performs faster and smaller than current models while being more scalable. |
Realistic Citation Count Prediction Task for Newly Published Papers (2023.findings-eacl)
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| 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. |
Dual Attention Model for Citation Recommendation (2020.coling-main)
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| Challenge: | Existing methods for recommending citations suffer from severe information loss . citation recommender methods do not consider the section of the paper for which the user is writing and for which they need to find a citation . |
| Approach: | They propose a novel embedding-based neural network to recommend citations during manuscript preparation. |
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A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications (N18-1)
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Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
| Challenge: | a dataset of 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR is presented to study peer reviews. |
| Approach: | They propose to use the dataset to collect peer reviews from top-tier venues including ACL, NIPS and ICLR and to use it to create a dataset of peer reviews for research purposes. |
| Outcome: | The proposed dataset includes 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. |
Related Work and Citation Text Generation: A Survey (2024.emnlp-main)
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| 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 . |
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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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Prototype-Based Interpretability for Legal Citation Prediction (2023.findings-acl)
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| Challenge: | citation prediction is a key problem in high-stakes decision making areas such as law . experts often require interpretability for automatic systems to be utilized in practical settings . |
| Approach: | They propose to use legal citation prediction to solve a problem with legal experts' feedback . they propose to add a prototype architecture to add interpretability while adhering to legal parameters . |
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Learning Neural Representation for CLIR with Adversarial Framework (D18-1)
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| Challenge: | Existing studies in cross-language information retrieval (CLIR) use general text representation models that are not optimized for the target task. |
| Approach: | They propose a novel text representation model based on adversarial learning which seeks a task-specific embedding space for CLIR. |
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Argument Mining for Understanding Peer Reviews (N19-1)
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| Challenge: | In 2015 alone, approximately 63.4 million hours were spent on peer reviews. |
| Approach: | They propose to automatically detect argumentative propositions put forward by reviewers and their types by automatically detecting their types and types. |
| Outcome: | The proposed method detects (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). |