D. Wang and T. Li, “Many are Better than One: Improving Multi-Document Summarization via Weighted Consensus,” Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, Geneva, Switzerland, 19-23 July 2010, pp. 809-810.

has been cited by the following article:

• TITLE: Multi-Document Summarization Model Based on Integer Linear Programming

  AUTHORS: Rasim Alguliev, Ramiz Aliguliyev, Makrufa Hajirahimova

  KEYWORDS: Multi-Document Summarization, Content Coverage, Less Redundancy, Integer Linear Programming

  JOURNAL NAME: Intelligent Control and Automation, Vol.1 No.2, November 26, 2010

  ABSTRACT: This paper proposes an extractive generic text summarization model that generates summaries by selecting sentences according to their scores. Sentence scores are calculated using their extensive coverage of the main content of the text, and summaries are created by extracting the highest scored sentences from the original document. The model formalized as a multiobjective integer programming problem. An advantage of this model is that it can cover the main content of source (s) and provide less redundancy in the generated sum- maries. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, have been used the similarity of sentences to the original document and the similarity between sentences. Performance evaluation is conducted by comparing summarization outputs with manual summaries of DUC2004 dataset. Experiments showed that the proposed approach outperforms the related methods.