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In this paper, we study the importance of context in predicting the citationworthiness of sentences in scholarly articles. We formulate this problem as asequence labeling task solved using a hierarchical BiLSTM model. We contributea new benchmark dataset containing over two million sentences and theircorresponding labels. We preserve the sentence order in this dataset andperform document-level train/test splits, which importantly allowsincorporating contextual information in the modeling process. We evaluate theproposed approach on three benchmark datasets. Our results quantify thebenefits of using context and contextual embeddings for citation worthiness.Lastly, through error analysis, we provide insights into cases where contextplays an essential role in predicting citation worthiness.

On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles