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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li
Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li
Computer Science Faculty Publications
The growth of scientific papers in the past decades calls for effective claim extraction tools to automatically and accurately locate key claims from unstructured text. Such claims will benefit content-wise aggregated exploration of scientific knowledge beyond the metadata level. One challenge of building such a model is how to effectively use limited labeled training data. In this paper, we compared transfer learning and contrastive learning frameworks in terms of performance, time and training data size. We found contrastive learning has better performance at a lower cost of data across all models. Our contrastive-learning-based model ClaimDistiller has the highest performance, boosting …
Theory Entity Extraction For Social And Behavioral Sciences Papers Using Distant Supervision, Xin Wei, Lamia Salsabil, Jian Wu
Theory Entity Extraction For Social And Behavioral Sciences Papers Using Distant Supervision, Xin Wei, Lamia Salsabil, Jian Wu
Computer Science Faculty Publications
Theories and models, which are common in scientific papers in almost all domains, usually provide the foundations of theoretical analysis and experiments. Understanding the use of theories and models can shed light on the credibility and reproducibility of research works. Compared with metadata, such as title, author, keywords, etc., theory extraction in scientific literature is rarely explored, especially for social and behavioral science (SBS) domains. One challenge of applying supervised learning methods is the lack of a large number of labeled samples for training. In this paper, we propose an automated framework based on distant supervision that leverages entity mentions …
Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles
Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles
Computer Science Faculty Publications
We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …