Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization
Panagiotis Kouris, Georgios Alexandridis, Andreas Stafylopatis · 2019
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.