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.
Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of citations prediction with relatively small training data sets, or larger datasets but with short, incomplete input text. In this work we leverage the open access ACL Anthology collection in combination with the Semantic Scholar bibliometric database…
Citation function and citation sentiment are two essential aspects of citation content analysis (CCA), which are useful for influence analysis, the recommendation of scientific publications. However, existing studies are mostly traditional machine learning methods, although deep learning techniques have also been explored, the improvement of the performance seems not significant due to insufficient training data, which brings difficulties to applications. In this paper, we propose to fine-tune pre-trained contextual embeddings ULMFiT, BERT, and XLNet for the task. Experiments on three public d…
Scientific document understanding is challenging as the data is highly domain specific and diverse. However, datasets for tasks with scientific text require expensive manual annotation and tend to be small and limited to only one or a few fields. At the same time, scientific documents contain many potential training signals, such as citations, which can be used to build large labelled datasets. Given this, we present an in-depth study of cite-worthiness detection in English, where a sentence is labelled for whether or not it cites an external source. To accomplish this, we introduce CiteWorth,…
Structurally-bounded Agentic Graph Exploration for Evidence-Grounded Scholarly DeepSearch
National University of Singapore（新加坡国立大学）；TCG CREST；Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Singapore Institute of Technology（新加坡理工学院）；Indian Institute of Technology Bombay（印度理工学院孟买分校）
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Leibniz Information Centre for Science and Technology（莱布尼茨科学技术信息中心）
Information extraction from scholarly articles is a challenging task due to the sizable document length and implicit information hidden in text, figures, and citations. Scholarly information extraction has various applications in exploration, archival, and curation services for digital libraries and knowledge management systems. We present MORTY, an information extraction technique that creates structured summaries of text from scholarly articles. Our approach condenses the article's full-text to property-value pairs as a segmented text snippet called structured summary. We also present a siza…
CiteFusion: An Ensemble Framework for Citation Intent Classification Harnessing Dual-Model Binary Couples and SHAP Analyses
University of Bologna（博洛尼亚大学）；Technical University of Dresden（德累斯顿工业大学）；Institute for Applied Computer Science, InfAI（InfAI应用计算机科学研究所）；SCaDS.AI center（SCaDS.AI中心）
Graduate School of Education, Stanford University（斯坦福大学教育研究生院）；Public Knowledge Project, Simon Fraser University（公共知识项目，西蒙弗雷泽大学）；DataCite, Hannover, Germany（DataCite，德国汉诺威）；California Digital Library, University of California Office of the President（加州数字图书馆，加州大学校长办公室）
University of Mannheim（曼海姆大学）；University of Washington（华盛顿大学）；Allen Institute for AI（艾伦人工智能研究所）；University of Michigan（密歇根大学）
Citation context analysis (CCA) is an important task in natural language processing that studies how and why scholars discuss each others' work. Despite decades of study, traditional frameworks for CCA have largely relied on overly-simplistic assumptions of how authors cite, which ignore several important phenomena. For instance, scholarly papers often contain rich discussions of cited work that span multiple sentences and express multiple intents concurrently. Yet, CCA is typically approached as a single-sentence, single-label classification task, and thus existing datasets fail to capture th…
