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Exploiting User Comments for Document Summarization with Matrix Factorization

Published: 04 December 2019 Publication History

Abstract

Social media presents a new method for readers who can freely discuss the content of an event mentioned in a Web document by posting relevant comments. The comments provide additional information which can be used to enrich the information of the main document. This paper introduces a new model which integrates user comments into the summarization process. While prior methods consider the same topic number between sentences and comments of a document, we argue that sentences and comments should own their different topics and they also share common hidden topics in term of same or inferred words. From this, we define a new objective function which jointly combines sentences and comments to achieve global optimization. The objective function is optimized by our non-negative matrix factorization algorithm to find out weights of sentence-matrix and comment-matrix for ranking sentences and comments. Experimental results on two datasets in English and Vietnamese show that our model achieves promising results for single-document summarization.

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  • (2021)Picture Preview Generation for Interactive Educational ResourcesComplexity10.1155/2021/55368672021(1-14)Online publication date: 12-May-2021
  • (2020)Preview Generation for Mathematical Interactive Educational Resources in Netpad2020 8th International Conference on Digital Home (ICDH)10.1109/ICDH51081.2020.00045(221-226)Online publication date: Sep-2020

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cover image ACM Other conferences
SoICT '19: Proceedings of the 10th International Symposium on Information and Communication Technology
December 2019
551 pages
ISBN:9781450372459
DOI:10.1145/3368926
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • SOICT: School of Information and Communication Technology - HUST
  • NAFOSTED: The National Foundation for Science and Technology Development

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2019

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Author Tags

  1. Matrix Factorization
  2. Natural Language Understanding
  3. Social Context Summarization
  4. Summarization

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  • Research-article
  • Research
  • Refereed limited

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  • UTEHY.L.2019.53

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SoICT 2019

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

View all
  • (2021)Picture Preview Generation for Interactive Educational ResourcesComplexity10.1155/2021/55368672021(1-14)Online publication date: 12-May-2021
  • (2020)Preview Generation for Mathematical Interactive Educational Resources in Netpad2020 8th International Conference on Digital Home (ICDH)10.1109/ICDH51081.2020.00045(221-226)Online publication date: Sep-2020

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