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K. M. Svore, L. Vanderwende, and C. J. C. Burges, “Enhancing single-document summarization by combining RankNet and third-party sources,” in: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL’07), Prague, Czech Republic, pp. 448–457, 28–30 June 2007.

has been cited by the following article:

• TITLE: Evolutionary Algorithm for Extractive Text Summarization

  AUTHORS: Rasim ALGULIEV, Ramiz ALIGULIYEV

  KEYWORDS: sentence clustering, document summarization, discrete differential evolution algorithm

  JOURNAL NAME: Intelligent Information Management, Vol.1 No.2, November 30, 2009

  ABSTRACT: Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization may compose novel sentences, unseen in the original sources. In our study we focus on sentence based extractive document summarization. The extractive summarization systems are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document. In this paper, we propose unsupervised document summarization method that creates the summary by clustering and extracting sentences from the original document. For this purpose new criterion functions for sentence clustering have been proposed. Similarity measures play an increasingly important role in document clustering. Here we’ve also developed a discrete differential evolution algorithm to optimize the criterion functions. The experimental results show that our suggested approach can improve the performance compared to sate-of-the-art summarization approaches.