AlMazrouei, RZ 
ORCID: https://orcid.org/0000-0003-2846-2322, Nelci, J ORCID: https://orcid.org/0000-0001-5467-0593, Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 and Shaalan, K ORCID: https://orcid.org/0000-0003-0823-8390
2022,
Feasibility of using attention mechanism in abstractive summarization
, in: International Conference on Emerging Technologies and Intelligent Systems (ICETIS), 25th-26th June 2021, Al Buraimi, Oman.
Abstract
The Prevalence of information and its magnitude mandates a short description of the core of a document, an article, or legal documents. Abstractive summarization helps to concur with this problem utilizing the evolutions in machine learning and deep neural network. Attention-mechanism has extensively applied in the challenging issue of abstraction a text, in shorter length yet informative. We noticed in [13] after removing the attention layer from their proposed model, the performance only experience soft drawback, even can be ignored. Thus, motivates us to survey the latest models using attention-mechanism and its achievements, and the second objective is to run an experiment to compare standard stacked 3- Long Short-Term Memory (LSTM) layers incorporated with attention layer only (without any other hand-crafted algorithm) to explore how efficient this technique can generate better summarization, then a stand-alone model. The standard proposed model incorporated with attention-mechanism suffered from drawback performance and scored less than a stand-alone model by at least 6 point scores on ROUGE-1&2.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Schools: | Schools > School of Computing, Science and Engineering |
| Journal or Publication Title: | Lecture Notes in Networks and Systems |
| Publisher: | Springer |
| Series Name: | Lecture Notes in Networks and Systems |
| ISBN: | 9783030826154 (paperback); 9783030826161 (ebook) |
| ISSN: | 2367-3370 |
| Related URLs: | |
| Depositing User: | USIR Admin |
| Date Deposited: | 30 Nov 2021 10:14 |
| Last Modified: | 30 Nov 2021 10:14 |
| URI: | http://usir.salford.ac.uk/id/eprint/62445 |
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