@inproceedings{7efbb22226004bc8bfc8a34d72db9739,
title = "Text understanding with a hybrid neural network based learning",
abstract = "Teaching machine to understand needs to design an algorithm for the machine to comprehend documents. As some traditional methods cannot learn the inherent characters effectively, this paper presents a new hybrid neural network model to extract sentence-level summarization from single document, and it allows us to develop an attention based deep neural network that can learn to understand documents with minimal prior knowledge. The proposed model composed of multiple processing layers can learn the representations of features. Word embedding is used to learn continuous word representations for constructing sentence as input to convolutional neural network. The recurrent neural network is also used to label the sentences from the original document, and the proposed BAM-GRU model is more efficient. Experimental results show the feasibility of the approach. Some problems and further works are also present in the end.",
keywords = "Convolutional neural network, Deep learning, Gated recurrent unit, Recurrent neural network, Word embedding",
author = "Shen Gao and Huaping Zhang and Kai Gao",
note = "Publisher Copyright: {\textcopyright} 2017, Springer Nature Singapore Pte Ltd.; 3rd International Conference of Pioneer Computer Scientists, Engineers, and Educators, ICPCSEE 2017 ; Conference date: 22-09-2017 Through 24-09-2017",
year = "2017",
doi = "10.1007/978-981-10-6388-6_10",
language = "English",
isbn = "9789811063879",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "115--125",
editor = "Qilong Han and Beiji Zou and Xiaoning Peng and Zeguang Lu and Guanglu Sun and Weipeng Jing",
booktitle = "Data Science - 3rd International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Proceedings",
address = "Germany",
}