Chapter

Computational Linguistics and Intelligent Text Processing

Volume 8404 of the series Lecture Notes in Computer Science pp 466-480

Graph Ranking on Maximal Frequent Sequences for Single Extractive Text Summarization

  • Yulia LedenevaAffiliated withUnidad Académica Profesional Tianguistenco, Universidad Autónoma del Estado de México
  • , René Arnulfo García-HernándezAffiliated withUnidad Académica Profesional Tianguistenco, Universidad Autónoma del Estado de México
  • , Alexander GelbukhAffiliated withCentro de Investigación en Computación, Instituto Politécnico Nacional

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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.