PPT-Diversity driven Attention Model for Query-based Abstractive Summarization

Author : sherrill-nordquist | Published Date : 2019-11-28

Diversity driven Attention Model for Querybased Abstractive Summarization Preksha Nema Mitesh Khapra Anirban Laha Balaraman Ravindran Indian Institute

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Diversity driven Attention Model for Query-based Abstractive Summarization: Transcript

Diversity driven Attention Model for Querybased Abstractive Summarization Preksha Nema Mitesh Khapra Anirban Laha Balaraman Ravindran Indian Institute of Technology Madras India. 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 (. video -Object driven Vs. Story driven. Presented By: Elad Osherov Jan 2013. Today’s talk. Motivation. Related Work. Object driven summarization. Story driven summarization. Results. Future Development. 134CHAPTER13.ASTROPHYSICALJETSwinds:jetsmightbepressure-driven,radiation-driven,Alfven-wave-driven,orshock-driven.Nooneissurehowjetsarecollimated;bythetimetheyarevisibletoobservers,theyarealreadytight Davide Mottin, University of Trento. Francesco . Bonchi. , Yahoo Labs - Francesco . Gullo. , Yahoo Labs. Issues. with Pattern . S. earch. O. OH. O. S. Query. 510 . matches. T. oo. . many. . matches. Jie Tang. *. , Limin Yao. #. , and Dewei Chen. *. *. Dept. of Computer Science and Technology. Tsinghua University. #. Dept. of Computer Science, University of Massachusetts Amherst. April, 2009. ?. What are the major topics in the returned docs?. 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. By . : . asef. . poormasoomi. Supervisor. : Dr. . Kahani. autumn 2010. Ferdowsi. University of . Mashad. Introduction. summary. : . brief. but . accurate. representation of the . contents. of a document. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction. Julia Freeman and Souleiman Ayoub. Overview. Customizing a filled out template provided by Fusion. . Summarize news articles with attributes that are extracted. Named entities especially for organizations and persons. Date. ： . 2014/08/11. Author . ： . Lidan. . Shou. , . Zhenhua. Wang, . Ke. Chen, Gang Chen. Source. ： . SIGIR’13. Advisor: . Jia. -ling . Koh. Speaker. ： . Sz-Han,Wang. O. utline. Introduction. 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. 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. Tal . Baumel. , Rafi Cohen, Michael Elhadad. Jan 2014. Generic Summarization. Generic Extractive Multi-doc Summarization:. Given a set of documents Di. Identify a set of sentences . Sj. . s.t.. |. Sj. Driven Well Construction Features Assembled lengths of two inches to three inches diameter metal pipes are driven into the ground A screened well point located at the end of the pipe helps drive the

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