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However, previous methods based on GNN are mainly faced with the practical problems of fixed corpus level graph structure which do not support online testing and high memory consumption. To tackle the problems, we propose a new GNN based model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. This method removes the burden of dependence between an individual text and entire corpus which support online testing, but still preserve global information. Besides, we build graphs by much smaller windows in the text, which not only extract more local features but also significantly reduce the edge numbers as well as memory consumption. Experiments show that our model outperforms existing models on several text classification datasets even with consuming less memory.","url_abs":"https://arxiv.org/abs/1910.02356v2","url_pdf":"https://arxiv.org/pdf/1910.02356v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"text-level-graph-neural-network-for-text","repo_url":"https://github.com/mojave-pku/TextLevelGCN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"text-level-graph-neural-network-for-text","repo_url":"https://github.com/Cynwell/Text-Level-GNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-classification-on-ohsumed","task":"Text Classification","dataset":"Ohsumed","model":"Our Model*","rank_in_archive_order":2,"of":10,"metrics":{"Accuracy":"69.4"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-r52","task":"Text Classification","dataset":"R52","model":"Our Model*","rank_in_archive_order":3,"of":8,"metrics":{"Accuracy":"94.6"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-r8","task":"Text Classification","dataset":"R8","model":"Our Model*","rank_in_archive_order":9,"of":21,"metrics":{"Accuracy":"97.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.02356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02356"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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