Summary of Graph Neural Network and Ner-based Text Summarization, by Imaad Zaffar Khan et al.
Graph Neural Network and NER-Based Text Summarization
by Imaad Zaffar Khan, Amaan Aijaz Sheikh, Utkarsh Sinha
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach to text summarization leverages Graph Neural Networks (GNNs) and Named Entity Recognition (NER) systems to efficiently compress lengthy documents into shorter representations while preserving core information. GNNs excel at understanding complex document structures, while NER systems emphasize key entities. By integrating these technologies, the method aims to enhance summarization efficiency and relevance in condensed content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach helps handle the increasing volume of textual data by identifying important information and summarizing it into a concise representation. It’s like using AI-powered highlights to help us quickly grasp the main points of a long article or document. |
Keywords
* Artificial intelligence * Named entity recognition * Ner * Summarization
