Sentence embedding approach using LSTM auto-encoder for discussion threads summarization
Computer Science and Information Systems, Tome 20 (2023) no. 4
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/}
}
TY - JOUR AU - Abdul Wali Khan AU - Feras Al-Obeidat AU - Afsheen Khalid AU - Adnan Amin AU - Fernando Moreira TI - Sentence embedding approach using LSTM auto-encoder for discussion threads summarization JO - Computer Science and Information Systems PY - 2023 VL - 20 IS - 4 UR - http://geodesic.mathdoc.fr/item/CSIS_2023_20_4_a6/ ID - CSIS_2023_20_4_a6 ER -
%0 Journal Article %A Abdul Wali Khan %A Feras Al-Obeidat %A Afsheen Khalid %A Adnan Amin %A Fernando Moreira %T Sentence embedding approach using LSTM auto-encoder for discussion threads summarization %J Computer Science and Information Systems %D 2023 %V 20 %N 4 %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/