@inproceedings{b4399b6df3ef4436a4cb72c21894e7e2,
title = "Multilayer CARU Model for Text Summarization",
abstract = "With the large amount of data available today, text summaries have become very important to get the right amount of information from a large amount of text. As can be seen, the articles in this review present different approaches to creating long summaries. Various studies have investigated different methods for summarizing text. In most cases, the methods described in this paper produce summaries or excerpt summaries of text documents, and a query-based summarization technique is also described. These techniques are structure and semantic based methods for summarizing text documents. Experimental results on the DUC-2002 dataset show that our system outperforms state of-the-art extractive and abstractive baselines on the ROUGE evaluation metric.",
keywords = "CARU, Encode-Decode Neural Network, NLP, Selector, Summarization",
author = "Im, {Sio Kei} and Chan, {Ka Hou}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 ; Conference date: 13-02-2023 Through 16-02-2023",
year = "2023",
doi = "10.1109/BigComp57234.2023.00098",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "399--402",
editor = "Hyeran Byun and Ooi, {Beng Chin} and Katsumi Tanaka and Sang-Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young-guk Ha and Kazutoshi Sumiya and Wu Yuncheng and Hyuk-Yoon Kwon and Takehiro Yamamoto",
booktitle = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",
address = "United States",
}