Extractive Text-Based Summarization of Arabic Videos: Issues, Approaches and Evaluations

  • Mohamed Amine MenacerEmail author
  • Carlos-Emiliano González-Gallardo
  • Karima Abidi
  • Dominique Fohr
  • Denis Jouvet
  • David Langlois
  • Odile Mella
  • Fatiha Sadat
  • Juan-Manuel Torres-Moreno
  • Kamel Smaïli
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1108)


In this paper, we present and evaluate a method for extractive text-based summarization of Arabic videos. The algorithm is proposed in the scope of the AMIS project that aims at helping a user to understand videos given in a foreign language (Arabic). For that, the project proposes several strategies to translate and summarize the videos. One of them consists in transcribing the Arabic videos, summarizing the transcriptions, and translating the summary. In this paper we describe the video corpus that was collected from YouTube and present and evaluate the transcription-summarization part of this strategy. Moreover, we present the Automatic Speech Recognition (ASR) system used to transcribe the videos, and show how we adapted this system to the Algerian dialect. Then, we describe how we automatically segment into sentences the sequence of words provided by the ASR system, and how we summarize the obtained sequence of sentences. We evaluate objectively and subjectively our approach. Results show that the ASR system performs well in terms of Word Error Rate on MSA, but needs to be adapted for dealing with Algerian dialect data. The subjective evaluation shows the same behaviour than ASR: transcriptions for videos containing dialectal data were better scored than videos containing only MSA data. However, summaries based on transcriptions are not as well rated, even when transcriptions are better rated. Last, the study shows that features, such as the lengths of transcriptions and summaries, and the subjective score of transcriptions, explain only 31% of the subjective score of summaries.


Text summarization Video summarization Automatic speech recognition Segmentation 



We acknowledge the support of Chist-Era for funding this research through the AMIS (Access Multilingual Information opinionS) project.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohamed Amine Menacer
    • 1
    Email author
  • Carlos-Emiliano González-Gallardo
    • 2
  • Karima Abidi
    • 1
  • Dominique Fohr
    • 1
  • Denis Jouvet
    • 1
  • David Langlois
    • 1
  • Odile Mella
    • 1
  • Fatiha Sadat
    • 3
  • Juan-Manuel Torres-Moreno
    • 2
    • 4
  • Kamel Smaïli
    • 1
  1. 1.Loria, University of LorraineNancyFrance
  2. 2.LIA, Avignon UniversitéAvignonFrance
  3. 3.UQAMMontrealCanada
  4. 4.Poliyechnique MontréalMontrealCanada

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