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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 gSpan: Graph-Based Substructure Pattern Mining Xifeng Yan; Jiawei Han In: http://www-faculty.cs.uiuc.edu/~hanj/pdf/icdm02_gspan.pdf (2002) BASE Show details 2 on the paper ”OPINOSIS- A Graph-Based Approach to Abstractive Summarization of Highly Redundant Opinions” by Kavita Ganesan; Chengxiang Zhai; Jiawei Han... In: http://www.mpi-inf.mpg.de/departments/d5/teaching/ws10_11/hir/reports/CosminaCroitoru.pdf BASE Show details 3 � Problems Kavita Ganesan; Chengxiang Zhai; Jiawei Han In: http://kavita-ganesan.com/sites/default/files/opinosis-presentation.ppt.pdf BASE Show details 4 Semantic Frame-Based Document Representation for Comparable Corpora Hyungsul Kim; Xiang Ren; Yizhou Sun... In: http://web.engr.illinois.edu/~hanj/pdf/icdm13_hkim.pdf BASE Show details 5 iTopicModel: Information Network-Integrated Topic Modeling Yizhou Sun; Jiawei Han; Jing Gao... In: http://www.cs.uiuc.edu/homes/hanj/pdf/icdm09_ysun.pdf BASE Show details 6 Content Coverage Maximization on Word Networks for Hierarchical Topic Summarization Chi Wang; Chengxiang Zhai; Xiao Yu; Jiawei Han; Yanen Li In: http://www.cs.uiuc.edu/homes/hanj/pdf/cikm13_cwang.pdf Abstract: This paper studies text summarization by extracting hierarchical topics from a given collection of documents. We propose a new approach of text modeling via network analysis. We convert documents into a word influence network, and find the words summarizing the major topics with an efficient influence maximization algorithm. Besides, the influence capability of the topic words on other words in the network reveal the relations among the topic words. Then we cluster the words and build hierarchies for the topics. Experiments on large collections of Web documents show that a simple method based on the influence analysis is effective, compared with existing generative topic modeling and random walk based ranking. Keyword: Data Mining General Terms Algorithms; Document and Text Processing; Experimentation Keywords Information Coverage; H.2.8 [Database Applications; I.7 [Computing Methodologies; Keyword Extraction; Text Summarization; Topic Hierarchy URL: http://www.cs.uiuc.edu/homes/hanj/pdf/cikm13_cwang.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.384.8917 BASE Hide details 7 The Wisdom of Minority: Unsupervised Slot Filling Validation based on Multi-dimensional Truth-Finding Dian Yu; Hongzhao Huang; Taylor Cassidy... In: http://aclweb.org/anthology/C/C14/C14-1149.pdf BASE Show details 8 SCENE: Structural Conversation Evolution NEtwork Marina Danilevsky; Joshua Hailpern; Jiawei Han In: http://www.cs.uiuc.edu/%7Ehanj/pdf/asonam11_mdanilevsky.pdf BASE Show details 9 Exploiting Background Information Networks to Enhance Bilingual Event Extraction Through Topic Modeling Hao Li; Heng Ji; Hongbo Deng... In: http://www.thinkmind.org/download.php?articleid=immm_2011_1_40_20203 BASE Show details

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