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

• URL: http://arxiv.org/abs/2104.08962v1
• Date: Sun, 18 Apr 2021 21:47:30 GMT
• Title: On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles
• Authors: Rakesh Gosangi, Ravneet Arora, Mohsen Gheisarieha, Debanjan Mahata, Haimin Zhang
• Abstract summary: 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. Our results quantify the benefits of using context and contextual embeddings for citation worthiness.
• Score: 10.28696219236292
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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