{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graph-based-neural-multi-document","title":"Graph-based Neural Multi-Document Summarization","arxiv_id":"1706.06681","date":"2017-06-20","proceeding":"CONLL 2017 8","authors":["Michihiro Yasunaga","Rui Zhang","Kshitijh Meelu","Ayush Pareek","Krishnan Srinivasan","Dragomir Radev"],"abstract":"We propose a neural multi-document summarization (MDS) system that\nincorporates sentence relation graphs. We employ a Graph Convolutional Network\n(GCN) on the relation graphs, with sentence embeddings obtained from Recurrent\nNeural Networks as input node features. Through multiple layer-wise\npropagation, the GCN generates high-level hidden sentence features for salience\nestimation. We then use a greedy heuristic to extract salient sentences while\navoiding redundancy. In our experiments on DUC 2004, we consider three types of\nsentence relation graphs and demonstrate the advantage of combining sentence\nrelations in graphs with the representation power of deep neural networks. Our\nmodel improves upon traditional graph-based extractive approaches and the\nvanilla GRU sequence model with no graph, and it achieves competitive results\nagainst other state-of-the-art multi-document summarization systems.","url_abs":"http://arxiv.org/abs/1706.06681v3","url_pdf":"http://arxiv.org/pdf/1706.06681v3.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":[],"tasks":[{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"multi-document-summarization","task_name":"Multi-Document Summarization"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"}],"methods":[{"method_slug":"gcn","method_name":"GCN"},{"method_slug":"gru","method_name":"GRU"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-document-summarization-on-duc-2004","task":"Multi-Document Summarization","dataset":"DUC 2004","model":"GCN: Personalized Discourse Graph","rank_in_archive_order":1,"of":1,"metrics":{"ROUGE-1":"38.23"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.06681","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}