Graph Ranking on Maximal Frequent Sequences for Single Extractive Text Summarization

  • Yulia Ledeneva
  • René Arnulfo García-Hernández
  • Alexander Gelbukh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8404)


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.


Term Selection Term Weighting Word Sense Disambiguation Text Summarization Document Summarization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© 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

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