A hybrid deep learning architecture for opinion-oriented multi-document summarization based on multi-feature fusion

Abdi, Asad and Hasan, Shafaatunnur and Shamsuddin, Siti Mariyam and Idris, Norisma and Piran, Jalil (2021) A hybrid deep learning architecture for opinion-oriented multi-document summarization based on multi-feature fusion. Knowledge-Based Systems, 213. ISSN 0950-7051,

Full text not available from this repository.
Official URL: https://doi.org/10.1016/j.knosys.2020.106658


Opinion summarization is a process to produce concise summaries from a large number of opinionated texts. In this paper, we present a novel deep-learning-based method for the generic opinion-oriented extractive summarization of multi-documents (also known as RDLS). The method comprises sentiment analysis embedding space (SAS), text summarization embedding spaces (TSS) and opinion summarizer module (OSM). SAS employs recurrent neural network (RNN) which is composed by long shortterm memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about a word have vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the existing drawbacks. TSS exploits multiple sources of statistical and linguistic knowledge features to augment word-level embedding and extract a proper set of sentences from multiple documents. TSS also uses the Restricted Boltzmann Machine algorithm to enhance and optimize those features and improve resultant accuracy without losing any important information. OSM consists of two phases: sentence classification and sentence selection which work together to produce a useful summary. Experiment results show that RDLS outperforms other existing methods. Moreover, the ensemble of statistical and linguistic knowledge, sentiment knowledge, sentiment shifter rules and word-embedding model allows RLDS to achieve significant accuracy. (C) 2021 The Authors. Published by Elsevier B.V.

Item Type: Article
Funders: Ministry of Higher Education (MOHE), Malaysia (Q.J130000.21A2.03E53) (04G48) (03G91) (13H82) (17H62), Research Management Centre (RMC), ASEAN-India Collaborative R&D Program (AISTDF), Cyber-Physical System research group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM)
Uncontrolled Keywords: Deep learning; Sentiment analysis; Opinion summarization; Linguistic knowledge; Recurrent neural network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Feb 2022 05:42
Last Modified: 23 Feb 2022 05:42
URI: http://eprints.um.edu.my/id/eprint/26840

Actions (login required)

View Item View Item