PPT-Supervised Spoken Document Summarization Based on Structure

Author : debby-jeon | Published Date : 2016-08-07

Author Sz rung Shiang HungYI Lee Lin shan Lee Speaker Sz rung Shiang National Taiwan University Outline Introduction Extractive summarization Structured support

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Supervised Spoken Document Summarization Based on Structure: Transcript

Author Sz rung Shiang HungYI Lee Lin shan Lee Speaker Sz rung Shiang National Taiwan University Outline Introduction Extractive summarization Structured support vector machine. 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 (. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. The new form of storytelling. Spoken Word Poetry is different . Spoken Word is not like the standard poetry that you learn about in ELA. It has soul and feeling that is easily identifiable. Spoken Word. 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. Classification. with Incomplete Class . Hierarchies. Bhavana Dalvi. ¶. *. , Aditya Mishra. †. , and William W. Cohen. *. ¶ . Allen Institute . for . Artificial Intelligence, . * . School Of Computer Science. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction. Dena B. French, . EdD. , RDN, . LD. ISPP Program Director & Experiential Coordinator. ISPP Class of 2017. Objectives. What is an ISPP?. Fontbonne’s. ISPP. Campus . “Tour”. Program overview & curriculum . Diversity driven Attention Model for Query-based Abstractive Summarization Preksha Nema *, Mitesh Khapra *, Anirban Laha* # , Balaraman Ravindran * * Indian Institute of Technology Madras, India 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. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization + DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction. Unsu. pervised . approaches . for . word sense disambiguation. Under the guidance of. Slides by. Arindam. . Chatterjee. &. Salil. Joshi. Prof. . Pushpak . Bhattacharyya. May 01, 2010. roadmap. Bird’s Eye View.. with Incomplete Class Hierarchies. Bhavana Dalvi. , Aditya Mishra, William W. Cohen. Semi-supervised Entity Classification. 2. Semi-supervised Entity Classification. Subset. 3. Disjoint. Semi-supervised Entity Classification. Authors: . Kexiang. Wang, . Zhifang. Sui, et al.. Organization: Peking University. Speaker: . Kexiang. Wang. E-mail: wkx@pku.edu.cn. Outline. Overview of Our Paper. Aim. We propose the adjustable affinity-preserving random walk method for generic and query-focused multi-document summarization to enforce the .

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