| 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. |
| Outcome: | The proposed method can recommend citations during manuscript preparation. |
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| Challenge: | Recent topic models that capture the time-series evolution of topics assume that topics evolve independently without interaction. |
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Multilevel Text Alignment with Cross-Document Attention (2020.emnlp-main)
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Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study (2020.acl-main)
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ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation (2024.lrec-main)
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A Hierarchical Neural Attention-based Text Classifier (D18-1)
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Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction (N18-1)
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Embedding Meta-Textual Information for Improved Learning to Rank (2020.coling-main)
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