Sentence embedding approach using LSTM auto-encoder for discussion threads summarization

Abdul Wali Khan1, Feras Al-Obeidat2, Afsheen Khalid1, Adnan Amin1 and Fernando Moreira3

  1. Center for Excellence in Information Technology,Institute of Management Sciences
    Peshawar, Pakistan,(adnan.amin, afsheen.khalid)
  2. College of Technological Innovation, Zayed University
    Abu Dhabi, UAE
  3. REMIT, IJP, Universidade Portucalense IEETA, Universidade de Aveiro


Online discussion forums are repositories of valuable information where users interact and articulate their ideas, opinions, and share experiences about numerous topics. They are internet-based online communities where users can ask for help and find the solution to a problem. On online discussion forums, a new user becomes exhausted from reading the significant number of replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome this limitation, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of average precision, recall, and F-measure of the proposed approach with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets proves the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks by boosting the performance of the automated DTS model.

Key words

Sentence embedding, LSTM Auto-encoder, CBOW, Deep learning, Machine learning, NLP

Digital Object Identifier (DOI)

Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Khan, A. W., Al-Obeidat, F., Khalid, A., Amin, A., Moreira, F.: Sentence embedding approach using LSTM auto-encoder for discussion threads summarization. Computer Science and Information Systems, Vol. 20, No. 4. (2023),