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

• Main Author: Khan, Abdul Wali
• Publication Date: 2023
• Other Authors: Al-Obeidat, Feras, Khalid, Afsheen, Adnan, Amin, Moreira, Fernando
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/11328/5061; https://doi.org/10.2298/CSIS221210055K
• topic: Sentence embedding; LSTM Auto-encoder; CBOW; Deep learning; Machine learning; NLP

Online discussion forums are repositories of valuable information where users interact and articulate their ideas, opinions, and share experiences about nu merous 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 per formance 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.