Papers › Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization
Panagiotis Kouris, Alex, Georgios ridis, Andreas Stafylopatis
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.
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