@conference {kouris-etal-2019-abstractive,
	title = {Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization},
	booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
	year = {2019},
	pages = {5082{\textendash}5092},
	publisher = {Association for Computational Linguistics},
	organization = {Association for Computational Linguistics},
	address = {Florence, Italy},
	abstract = {This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.},
	doi = {10.18653/v1/P19-1501},
	url = {https://aclanthology.org/P19-1501},
	author = {Kouris, Panagiotis and Alexandridis, Georgios and Stafylopatis, Andreas},
	editor = {Korhonen, Anna and Traum, David and M{\`a}rquez, Llu{\'{\i}s}
}
