PPT-Document Summarization Abhirut Gupta Mandar Joshi Piyush Dungarwal

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. 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 (. Prevalence of ASD . in Research Population of Youth . with . Bipolar Disorder . Total N:. 155. Age . (. yrs. ). :. 10.5 ±3.2 . (6. -17). Male:. 79%. IQ:. 103 ±14 (73 – 146). Statistical Significance. By . : . asef. . poormasoomi. Supervisor. : Dr. . Kahani. autumn 2010. Ferdowsi. University of . Mashad. Introduction. summary. : . brief. but . accurate. representation of the . contents. of a document. Overview. Ling573. Systems & Applications. March 31. , 2016. Roadmap. Dimensions . of the problem. Architecture . of a Summarization system. Summarization and resources. Evaluation. Logistics Check-. Reviews & Speech. Ling 573. Systems and Applications. May . 26, 2016. Roadmap. Abstractive summarization example. Using Abstract Meaning Representation. Review . summarization:. Basic approach. Learning what users want. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. 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. 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. 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. 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. and Organization for User-content Interaction. References. : 1. “Spoken Document Understanding and Organization”, IEEE Signal . Processing Magazine, Sept. 2005, Special Issue on Speech Technology. 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. What is epilepsy?. Why should we learn about epilepsy?. Medical aspects of epilepsy. Common myths and misconceptions about epilepsy. What can you do to mitigate the problem?. Summary . 2. Dr R P Joshi. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization + DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction.