Multi Document Summarization Based On Cross-Document Relation Using Voting Technique

News articles which are available through online search often provide readers with large collection of texts. Especially in the case of news story, different news sources reporting on the same event usually returns multiple articles in response to a reader’s search. In this work, we first identify cross-document relations from un-annotated texts using Genetic-CBR approach. Following that, we develop a new sentence scoring model based on voting technique over the identified cross-document relations. Our experiments show that incorporating the proposed methods in the summarization process yields substantial improvement over the mainstream methods. The performances of all methods were evaluated using ROUGE—a standard evaluation metric used in text summarization

A Genetic-CBR Approach for Cross-Document Relationship Identification

Various applications concerning multi document has emerged recently. Information across topically related documents can often be linked. Cross-document Structure Theory (CST) analyzes the relationships that exist between sentences across related documents. However, most of the existing works rely on human experts to identify the CST relationships. In this work, we aim to automatically identify some of the CST relations using supervised learning method. We propose Genetic-CBR approach which incorporates genetic algorithm (GA) to improve the case base reasoning (CBR) classification. GA is used to scale the weights of the data features used by the CBR classifier. We perform the experiments using the datasets obtained from CSTBank corpus. Comparison with other learning methods shows that the proposed method yields better results