Graph Ranking on Maximal Frequent Sequences for Single Extractive Text Summarization

  • Yulia Ledeneva
  • René Arnulfo García-Hernández
  • Alexander Gelbukh
Conference paper

DOI: 10.1007/978-3-642-54903-8_39

Volume 8404 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Ledeneva Y., García-Hernández R.A., Gelbukh A. (2014) Graph Ranking on Maximal Frequent Sequences for Single Extractive Text Summarization. In: Gelbukh A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg

Abstract

We suggest a new method for the task of extractive text summarization using graph-based ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a graph-based algorithm. We show that the proposed method produces results superior to the-state-of-the-art methods; in addition, the best sentences were found with this method. We prove that MFS are better than other terms. Moreover, we show that the longer is MFS, the better are the results. If the stop-words are excluded, we lose the sense of MFS, and the results are worse. Other important aspect of this method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, and languages.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yulia Ledeneva
    • 1
  • René Arnulfo García-Hernández
    • 1
  • Alexander Gelbukh
    • 2
  1. 1.Unidad Académica Profesional TianguistencoUniversidad Autónoma del Estado de MéxicoTolucaEstado de México
  2. 2.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico DFMexico