<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kouris, Panagiotis</style></author><author><style face="normal" font="default" size="100%">Alexandridis, Georgios</style></author><author><style face="normal" font="default" size="100%">Stafylopatis, Andreas</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Korhonen, Anna</style></author><author><style face="normal" font="default" size="100%">Traum, David</style></author><author><style face="normal" font="default" size="100%">Màrquez, Llu{\'ıs</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://aclanthology.org/P19-1501</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computational Linguistics</style></publisher><pub-location><style face="normal" font="default" size="100%">Florence, Italy</style></pub-location><pages><style face="normal" font="default" size="100%">5082–5092</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>