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A hybrid machine learning model for multi-document summarization

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Abstract

This work proposes an approach that uses statistical tools to improve content selection in multi-document automatic text summarization. The method uses a trainable summarizer, which takes into account several features: the similarity of words among sentences, the similarity of words among paragraphs, the text format, cue-phrases, a score related to the frequency of terms in the whole document, the title, sentence location and the occurrence of non-essential information. The effect of each of these sentence features on the summarization task is investigated. These features are then used in combination to construct text summarizer models based on a maximum entropy model, a naive-Bayes classifier, and a support vector machine. To produce the final summary, the three models are combined into a hybrid model that ranks the sentences in order of importance. The performance of this new method has been tested using the DUC 2002 data corpus. The effectiveness of this technique is measured using the ROUGE score, and the results are promising when compared with some existing techniques.

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Acknowledgement

This work is supported by the Deanship of Scientific Research, Taibah University, KSA.

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Authors and Affiliations

  1. Department of Computer Sciences, CCSE Taibah University, KSA, Almadina Almonawara, Saudi Arabia

    Mohamed Abdel Fattah

  2. Department of Electronics Technology, FIE Helwan University, Cairo, Egypt

    Mohamed Abdel Fattah

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  1. Mohamed Abdel Fattah
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Correspondence to Mohamed Abdel Fattah.

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Fattah, M.A. A hybrid machine learning model for multi-document summarization. Appl Intell 40, 592–600 (2014). https://doi.org/10.1007/s10489-013-0490-0

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