Local citation recommendation (LCR) suggests a set of papers for a citation placeholder within a given context. The task has evolved as generative approaches have become more promising than the traditional pre-fetch and re-rank-based state-of-the-art approaches. This paper introduces citation-specific pre-training within an encoder-decoder architecture, where author-date citation tokens are masked to learn to reconstruct them to fulfill LCR. There are two variants for this pre-training. In the local context-only base scheme (CiteBART-Base), the citation token in a local context is masked to learn to predict the citation. The global version (CiteBART-Global) extends the local context with the citing paper's title and abstract to enrich the learning signal. CiteBART-Global achieves state-of-the-art performance on LCR benchmarks except for the FullTextPeerRead dataset, which is quite small to see the advantage of generative pre-training. The effect is significant in the larger benchmarks, e.g., Refseer and ArXiv., with the Refseer benchmark-trained model emerging as the best-performing model. We perform comprehensive experiments, including an ablation study, a qualitative analysis, and a taxonomy of hallucinations with detailed statistics. Our analyses confirm that CiteBART-Global has a cross-dataset generalization capability; the macro hallucination rate (MaHR) at the top-3 predictions is 4\%, and when the ground-truth is in the top-k prediction list, the hallucination tendency in the other predictions drops significantly.
Scientist learn early on how to cite scientific sources to support their claims. Sometimes, however, scientists have challenges determining where a citation should be situated -- or, even worse, fail to cite a source altogether. Automatically detecting sentences that need a citation (i.e., citation worthiness) could solve both of these issues, leading to more robust and well-constructed scientific arguments. Previous researchers have applied machine learning to this task but have used small datasets and models that do not take advantage of recent algorithmic developments such as attention mechanisms in deep learning. We hypothesize that we can develop significantly accurate deep learning architectures that learn from large supervised datasets constructed from open access publications. In this work, we propose a Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism and contextual information to detect sentences that need citations. We also produce a new, large dataset (PMOA-CITE) based on PubMed Open Access Subset, which is orders of magnitude larger than previous datasets. Our experiments show that our architecture achieves state of the art performance on the standard ACL-ARC dataset ($F_{1}=0.507$) and exhibits high performance ($F_{1}=0.856$) on the new PMOA-CITE. Moreover, we show that it can transfer learning across these datasets. We further use interpretable models to illuminate how specific language is used to promote and inhibit citations. We discover that sections and surrounding sentences are crucial for our improved predictions. We further examined purported mispredictions of the model, and uncovered systematic human mistakes in citation behavior and source data. This opens the door for our model to check documents during pre-submission and pre-archival procedures. We make this new dataset, the code, and a web-based tool available to the community.
In legal document writing, one of the key elements is properly citing the case laws and other sources to substantiate claims and arguments. Understanding the legal domain and identifying appropriate citation context or cite-worthy sentences are challenging tasks that demand expensive manual annotation. The presence of jargon, language semantics, and high domain specificity makes legal language complex, making any associated legal task hard for automation. The current work focuses on the problem of citation-worthiness identification. It is designed as the initial step in today's citation recommendation systems to lighten the burden of extracting an adequate set of citation contexts. To accomplish this, we introduce a labeled dataset of 178M sentences for citation-worthiness detection in the legal domain from the Caselaw Access Project (CAP). The performance of various deep learning models was examined on this novel dataset. The domain-specific pre-trained model tends to outperform other models, with an 88% F1-score for the citation-worthiness detection task.
In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using a hierarchical BiLSTM model. We contribute a new benchmark dataset containing over two million sentences and their corresponding labels. We preserve the sentence order in this dataset and perform document-level train/test splits, which importantly allows incorporating contextual information in the modeling process. We evaluate the proposed approach on three benchmark datasets. Our results quantify the benefits of using context and contextual embeddings for citation worthiness. Lastly, through error analysis, we provide insights into cases where context plays an essential role in predicting citation worthiness.