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

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 CBOWmodel in sentence embedding tasks and boost the performance of the automated DTS model.