@inproceedings{072a20e82b9642139011aadd4dc10dbc,
title = "Multi-roles graph based extractive summarization",
abstract = "In this paper, we propose a multi-roles graph model for extractive single-document summarization. In our model, we consider that each text can be expressed in some important words which we call roles. We design three roles, including noun role, verb role and numeral role, and build a multi-roles graph according to these three roles to represent a text. And then we project this graph into three single role graphs according to the role of nodes. After that, we extract some import features from these four graphs by applying a modified PageRank algorithm and then combine them with some statistical features such as sentence position and the length of sentence to represent each sentence. Finally we train a random forest model to learn the pattern of selecting important sentences to generate summaries. To evaluate our model, we perform some experiments on DUC2001 and DUC2002 and achieve 13.9\% improvement over latest methods. Besides, we also obtain best results in ROUGE-2 compared with some classic methods.",
keywords = "Classification, Multi-roles graph, Random forest, Summarization",
author = "Zhibin Chen and Yunming Ye and Xiaofei Xu and Feng Li",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70087-8\_50",
language = "英语",
isbn = "9783319700861",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "474--483",
editor = "Yuanqing Li and Derong Liu and Shengli Xie and El-Alfy, \{El-Sayed M.\} and Dongbin Zhao",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "德国",
}