PPT-Affinity-Preserving Random Walk for Multi-Document Summarization

Authors Kexiang Wang Zhifang Sui et al Organization Peking University Speaker Kexiang Wang Email wkxpkueducn Outline Overview of Our Paper Aim We propose the adjustable affinitypreserving random walk method for generic and queryfocused multidocument summarization to enforce the . the Volume of Convex Bodies. By Group 7. The Problem Definition. The main result of the paper is a randomized algorithm for finding an approximation to the volume of a convex body . ĸ. in . n. -dimensional Euclidean space. E. asy-to-. U. nderstand English . Sum. maries for . Non-Native Readers. Authors : . Xiaojun. Wan (. 副研究員. ). http://www.icst.pku.edu.cn/intro/content_409.htm. Huiying. Li . Jianguo. Xiao (. and Semi-Supervised Learning. Longin Jan Latecki. Based on :. Xiaojin. Zhu. Semi-Supervised Learning with Graphs. PhD thesis. CMU-LTI-05-192, May 2005. Page, Lawrence and . Brin. , Sergey and . Motwani. spline. Methods for the Incompressible . Navier. -Stokes Equations. John Andrew Evans. Institute for Computational Engineering and Sciences, UT Austin. Stabilized and . Multiscale. . Methods in CFD. Lei Shi, Sibai Sun, . Yuan Xuan. , Yue Su, . Hanghang . Tong, Shuai Ma, Yang . Chen. Influence Graph. Initial. Tweet. Re-tweeting Graph. Re-tweets. Citing papers. Source. Paper. Paper Citation Graph. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. David . Harel. and . Yehuda. . Koren. KDD 2001. Introduction. Advances in database technologies resulted in huge amounts of spatial data. The characteristics of spatial data pose several difficulties for clustering algorithms.. ST. . CENTURY. Making Sure The Christian Faith Lasts And Maintaining It In The 21. st. Century. 2 . Peter 1: 5- 11. Bro. Josiah G. . Emonena. Preserving Our Christian Faith In The 21st Century. INTRODUCTION. 1. Wan-Ting Hsu. National Tsing Hua University. Chieh. -Kai Lin. National Tsing Hua University. Project page. Outline. Motivation. Our Method. Training Procedures. Experiments and Results. Conclusion. Kathleen McKeown. Department of Computer Science. Columbia University. What is Summarization?. Data as input (database, software trace, expert system), text summary as output. Text as input (one or more articles), paragraph summary as output. Presented By:. Humam. . Nameer. 1. CASE STUDY. Just over three years old, . Clash of Clans.  rakes in more than $. 5M.  each day and consistently charts in the top three grossing . apps. .  It’s the app that new game developers look to for . Ameet. Deshpande. March 24, 2020. TASK. Text Summarization is the reduction of data to a (minimal) subset which represents the original data. Two types of Summarization techniques. Extractive Summarization. Kathleen McKeown. Department of Computer Science. Columbia University. Today. HW3 assigned. Summarization (switch in order of topics). WEKA tutorial (for HW3). Midterms back. What is Summarization?. Data as input (database, software trace, expert system), text summary as output. Reddit. Posts. with Multi-level Memory Networks. . [. NAACL . 2019]. Group Presentation. WANG, Yue. 04/15/2019. Outline. Background. Dataset. Method. Experiment. Conclusion. 2. /16. Background. Challenge:.