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Extractive Single-Document Summarization Based on Global-Best Harmony Search and a Greedy Local Optimizer

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Advances in Artificial Intelligence and Its Applications (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9414))

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Abstract

Due to the great amount of documents available on the Web, end users need to be able to access information in summary form – keeping the most important information in the document. The methods employed for automatic text summarization generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. In this paper, the extractive single document summarization task is treated as a binary optimization problem and, based on the Global-best Harmony Search metaheuristic and a greedy local search procedure, a new algorithm called ESDS-GHS-GLO is proposed. This algorithm optimizes an objective function, which is a lineal normalized combination of the position of the sentence in the document, sentence length, and coverage of the selected sentences in the summary. The proposed method was compared with the state of the art methods MA-SingleDocSum, DE, FEOM, UnifiedRank, NetSum, QCS, CRF, SVM, and Manifold Ranking, using ROUGE measures on the DUC2001 and DUC2002 datasets. The results showed that ESDS-GHS-GLO outperforms most of the state-of-the-art methods except MA-SingleDocSum. ESDS-GHS-GLO obtains promissory results using a fitness function less complex than MA-SingleDocSum, therefore requiring less execution time.

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Acknowledgments

The work in this paper was supported by the University of Cauca and the National University of Colombia. We are especially grateful to Colin McLachlan for suggestions relating to English text.

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Authors and Affiliations

  1. Universidad Del Cauca, Popayán, Colombia

    Martha Mendoza & Carlos Cobos

  2. Universidad Nacional de Colombia, Bogotá D.C., Colombia

    Elizabeth León

Authors
  1. Martha Mendoza
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  2. Carlos Cobos
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  3. Elizabeth León
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Corresponding author

Correspondence to Martha Mendoza .

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Editors and Affiliations

  1. Unidad Profesional Interdisciplinaria, México DF, Mexico

    Obdulia Pichardo Lagunas

  2. Universidad Autónoma Metropolitana, México DF, Mexico

    Oscar Herrera Alcántara

  3. Instituto de Investigaciones Eléctricas, Cuernavaca, Morelos, Mexico

    Gustavo Arroyo Figueroa

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Mendoza, M., Cobos, C., León, E. (2015). Extractive Single-Document Summarization Based on Global-Best Harmony Search and a Greedy Local Optimizer. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-27101-9_4

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  • Publisher Name: Springer, Cham

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