Document Type

Article

Source of Publication

Computer Science and Information Systems

Publication Date

9-1-2023

Abstract

Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant 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 these limitations, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of the proposed approach’s average precision, recall, and F-measure with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets demonstrates the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks and boost the performance of the automated DTS model.

ISSN

1820-0214

Publisher

National Library of Serbia

Volume

20

Issue

4

First Page

1367

Last Page

1387

Disciplines

Computer Sciences

Keywords

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

Scopus ID

85174291979

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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