Multi document summarization based on news components using fuzzy cross-document relations

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.

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