Multi Document Summarization Based On News Components Using Fuzzy Cross-Document Relations

Yogan , Jaya Kumar and Naomie, Salim and Albaraa, Abuobieda and Ameer Tawfik, Albaham (2014) Multi Document Summarization Based On News Components Using Fuzzy Cross-Document Relations. Applied Soft Computing, 21. pp. 265-279. ISSN 1568-4946

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Online information is growing enormously day by day with the blessing of World Wide Web. Search engines often provide users with abundant collection of articles; in particular, news articles which are retrieved from different news sources reporting on the same event. In this work, we aim to produce high quality multi document news summaries by taking into account the generic components of a news story within a specific domain. We also present an effective method, named Genetic-Case Base Reasoning, to identify cross-document relations from un-annotated texts. Following that, we propose a new sentence scoring model based on fuzzy reasoning over the identified cross-document relations. The experimental findings show that the proposed approach performed better that the conventional graph based and cluster based approach.

Item Type: Article
Uncontrolled Keywords: Multi document summarization, News components, Cross-document structure theory (CST), Case-based reasoning, Genetic algorithm, Fuzzy logic
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Nor Aini Md. Jali
Date Deposited: 18 Mar 2016 02:31
Last Modified: 04 Sep 2021 19:46
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