Abstractive text summarization based on deep learning and semantic content generalization

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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

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