Abstractive text summarization based on deep learning and semantic
content generalization
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