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Exploiting User Posts for Web Document Summarization

Published: 08 June 2018 Publication History

Abstract

Relevant user posts such as comments or tweets of a Web document provide additional valuable information to enrich the content of this document. When creating user posts, readers tend to borrow salient words or phrases in sentences. This can be considered as word variation. This article proposes a framework that models the word variation aspect to enhance the quality of Web document summarization. Technically, the framework consists of two steps: scoring and selection. In the first step, the social information of a Web document such as user posts is exploited to model intra-relations and inter-relations in lexical and semantic levels. These relations are denoted by a mutual reinforcement similarity graph used to score each sentence and user post. After scoring, summaries are extracted by using a ranking approach or concept-based method formulated in the form of Integer Linear Programming. To confirm the efficiency of our framework, sentence and story highlight extraction tasks were taken as a case study on three datasets in two languages, English and Vietnamese. Experimental results show that: (i) the framework can improve ROUGE-scores compared to state-of-the-art baselines of social context summarization and (ii) the combination of the two relations benefits the sentence extraction of single Web documents.

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Cited By

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  • (2024)A method to utilize prior knowledge for extractive summarization based on pre-trained language modelsVietnam Journal of Science and Technology10.15625/2525-2518/20241Online publication date: 5-Dec-2024
  • (2021)Automatic Webpage Briefing2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00152(1727-1738)Online publication date: Apr-2021
  • (2019)Document Specific Supervised Keyphrase Extraction With Strong Semantic RelationsIEEE Access10.1109/ACCESS.2019.29488917(167507-167520)Online publication date: 2019

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Published In

cover image ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data  Volume 12, Issue 4
August 2018
354 pages
ISSN:1556-4681
EISSN:1556-472X
DOI:10.1145/3208362
Issue’s Table of Contents
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2018
Accepted: 01 February 2018
Revised: 01 January 2018
Received: 01 September 2017
Published in TKDD Volume 12, Issue 4

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Author Tags

  1. Data mining
  2. ILP
  3. information retrieval
  4. ranking
  5. social context summarization
  6. summarization

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • JSPS KAKENHI
  • Hung Yen University of Technology and Education; and QG.15.29
  • Vietnam National University, Hanoi (VNU)

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Cited By

View all
  • (2024)A method to utilize prior knowledge for extractive summarization based on pre-trained language modelsVietnam Journal of Science and Technology10.15625/2525-2518/20241Online publication date: 5-Dec-2024
  • (2021)Automatic Webpage Briefing2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00152(1727-1738)Online publication date: Apr-2021
  • (2019)Document Specific Supervised Keyphrase Extraction With Strong Semantic RelationsIEEE Access10.1109/ACCESS.2019.29488917(167507-167520)Online publication date: 2019

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