PPT-Multi-topic based Query-oriented Summarization

Author : celsa-spraggs | Published Date : 2017-01-18

Jie Tang Limin Yao and Dewei Chen Dept of Computer Science and Technology Tsinghua University Dept of Computer Science University of Massachusetts Amherst

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Multi-topic based Query-oriented Summarization: Transcript

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. Wenjie Zhang. Xuemin Lin, Muhammad Aamir Cheema, Ying Zhang, Wei Wang . The University of New South Wales, Australia. KNN Query. Find . k. objects closest to query . q. 2. q. o. 1. o. 4. o. 2. o. 3. 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. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. Access Pipeline Protests (NoDAPL). CS 5984/4984 Big Data Text Summarization Report. . Xiaoyu Chen*, Haitao Wang, Maanav Mehrotra, Naman Chhikara, Di Sun. {xiaoyuch, wanght, maanav, namanchhikara, sdi1995} @vt.edu. Document Summarization Abhirut Gupta Mandar Joshi Piyush Dungarwal Motivation The advent of WWW has created a large reservoir of data A short summary, which conveys the essence of the document, helps in finding relevant information quickly Diversity driven Attention Model for Query-based Abstractive Summarization Preksha Nema *, Mitesh Khapra *, Anirban Laha* # , Balaraman Ravindran * * Indian Institute of Technology Madras, India and Organization for User-content Interaction. References. : 1. “Spoken Document Understanding and Organization”, IEEE Signal . Processing Magazine, Sept. 2005, Special Issue on Speech Technology. Motivation. Experiments. Overall Framework. Multi-Abstraction Concern Localization. Tien-Duy B. Le, Shaowei Wang, and David Lo. {btdle.2012, shaoweiwang.2010,davidlo}@smu.edu.sg. Abstraction. Hierarchy. 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. 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:. Big Graphs. Arijit Khan. Nanyang Technological University, Singapore. Sourav S. Bhowmick. Nanyang Technological University, Singapore. Francesco Bonchi. ISI Foundation, Italy. Google: > 1 trillion indexed pages. Chapters 12.4-12.6 and 15.2-15.3 of Cow Book. Arun Kumar. CSE 132C. Database System Implementation. Slide ACKs: Jignesh Patel. Query. Query Result. Database Server. Select R.text from . Report R, Weather W. MS Thesis Defense. Manishkumar. . Jha. Committee Members. Dr. . Subbarao. . Kambhampati. Dr. . Huan. Liu. Dr. . Hasan. . Davulcu. 2. Deep Web Integration Scenario. Web DB. Mediator. ←. query. Web DB.

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