Infrastructure for Rapid Open Knowledge Network Development

Cafarella, Michael; Anderson, Michael E.; Beltagy, Iz; Cattan, Arie; Chasins, Sarah; Dagan, Ido; Downey, Doug; Etzioni, Oren; Feldman, Sergey; Tian, Ganghong; Hope, Tom; Huang, Kexin; Johnson, Sophie; King, Daniel; Lo, Kyle; Lou, Yuze; Shapiro, Matthew D.; Shen, Dinghao; Subramanian, Shivashankar; Wang, Lucy; Wang, Yuning; Wang, Yitong; Weld, Daniel S.; Vo-Phamhi, Jenny; Zeng, Anna; Zou, Jiayun

doi:10.1609/aimag.v43i1.19126

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…This often requires solving the entity disambiguation problem to uniquely identify entities under question—which is also a problem being addressed in the OKN‐related projects described in (Cafarella et al. 2022) and (Pah et al. 2022) in this special issue.…”

Section: Applications Of Knowledge Graphsmentioning

confidence: 99%

Knowledge graphs: Introduction, history, and perspectives

et al. 2022

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Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.

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Section: Applications Of Knowledge Graphsmentioning

confidence: 99%

Knowledge graphs: Introduction, history, and perspectives

et al. 2022

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Cross-lingual extreme summarization of scholarly documents

Takeshita,

Green,

Friedrich

et al. 2023

Int J Digit Libr

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The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage pipeline approach that independently summarizes and translates, as well as a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero- and few-shot scenarios. Finally, we investigate how to make our approach more efficient on the basis of knowledge distillation methods, which make it possible to shrink the size of our models, so as to reduce the computational complexity of the summarization inference.

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Infrastructure for Rapid Open Knowledge Network Development

Cited by 2 publications

References 2 publications

Knowledge graphs: Introduction, history, and perspectives

Knowledge graphs: Introduction, history, and perspectives

Cross-lingual extreme summarization of scholarly documents

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