fixing abstract

content/publication/Ramesh_Kashyap_Yang_Kan_2023/index.md

@@ -2,15 +2,15 @@
 title: 'Scientific document processing: challenges for modern learning methods'
 authors:
 - abhinav
-- yajing
+- Yang Yajing
 - min
 date: '2023-03-24'
 publishDate: '2024-07-06T02:22:24.568376Z'
 publication_types:
 - article-journal
 publication: '*International Journal on Digital Libraries*'
 doi: 10.1007/s00799-023-00352-7
-abstract: Neural network models enjoy success on language tasks related to Web documents, including news and Wikipedia articles. However, the characteristics of scientific publications pose specific challenges that have yet to be satisfactorily addressed: the discourse structure of scientific documents crucial in scholarly document processing (SDP) tasks, the interconnected nature of scientific documents, and their multimodal nature. We survey modern neural network learning methods that tackle these challenges: those that can model discourse structure and their interconnectivity and use their multimodal nature. We also highlight efforts to collect large-scale datasets and tools developed to enable effective deep learning deployment for SDP. We conclude with a discussion on upcoming trends and recommend future directions for pursuing neural natural language processing approaches for SDP.
+abstract: "Neural network models enjoy success on language tasks related to Web documents, including news and Wikipedia articles. However, the characteristics of scientific publications pose specific challenges that have yet to be satisfactorily addressed: the discourse structure of scientific documents crucial in scholarly document processing (SDP) tasks, the interconnected nature of scientific documents, and their multimodal nature. We survey modern neural network learning methods that tackle these challenges: those that can model discourse structure and their interconnectivity and use their multimodal nature. We also highlight efforts to collect large-scale datasets and tools developed to enable effective deep learning deployment for SDP. We conclude with a discussion on upcoming trends and recommend future directions for pursuing neural natural language processing approaches for SDP."
 links:
 - name: URL
   url: https://doi.org/10.1007/s00799-023-00352-7