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Sentence embedding approach using LSTM auto-encoder for discussion threads summarization
Computer Science and Information Systems, Tome 20 (2023) no. 4 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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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.
Keywords: Sentence embedding, LSTM Auto-encoder, CBOW, Deep learning, Machine learning, NLP 
@article{CSIS_2023_20_4_a6,
     author = {Abdul Wali Khan and Feras Al-Obeidat and Afsheen Khalid and Adnan Amin and Fernando Moreira},
     title = {Sentence embedding approach using {LSTM} auto-encoder for discussion threads summarization},
     journal = {Computer Science and Information Systems},
     year = {2023},
     volume = {20},
     number = {4},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a6/}
}
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AU  - Fernando Moreira
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%J Computer Science and Information Systems
%D 2023
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%U http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a6/
%F CSIS_2023_20_4_a6
Abdul Wali Khan; Feras Al-Obeidat; Afsheen Khalid; Adnan Amin; Fernando Moreira. Sentence embedding approach using LSTM auto-encoder for discussion threads summarization. Computer Science and Information Systems, Tome 20 (2023) no. 4. http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a6/