Abstractive text summarization based on deep learning and semantic content generalization

Kouris, Panagiotis; Alexandridis, Georgios; Stafylopatis, Andreas

dc.contributor.author	Kouris, Panagiotis
dc.contributor.author	Alexandridis, Georgios
dc.contributor.author	Stafylopatis, Andreas
dc.date.accessioned	2022-12-15T12:08:54Z
dc.date.available	2022-12-15T12:08:54Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/56459
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.24157
dc.rights	Default License
dc.subject	Abstractive Text Summarization	en
dc.subject	Deep Learning	en
dc.subject	Natural Language Processing	el
dc.title	Abstractive text summarization based on deep learning and semantic content generalization	en
heal.type	conferenceItem
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2019-07-28
heal.bibliographicCitation	Panagiotis Kouris, Georgios Alexandridis, and Andreas Stafylopatis. Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5082–5092, Florence, Italy. Association for Computational Linguistics (ACL), 2019..	el
heal.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.	en
heal.publisher	Association for Computational Linguistics (ACL)	el
heal.fullTextAvailability	false
heal.conferenceName	Association for Computational Linguistics (ACL)	en
heal.conferenceItemType	full paper
dc.identifier.doi	10.18653/v1/P19-1501	el