Abstractive text summarization: enhancing sequence to sequence models using word sense disambiguation and semantic content generalization

Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequence to sequence neural-based text summarization along with structure and semantic-based methodologies. The proposed framework is capable of dealing with the problem of out-of-vocabulary or rare words, improving the performance of the deep learning models. The overall methodology is based on a well defined theoretical model of knowledge-based content generalization and deeplearning predictions for generating abstractive summaries. The framework is comprised of three key elements: (i) a pre-processing task, (ii) a machine learning methodology and (iii) a post-processing task. The pre-processing task is a knowledge-based approach, based on ontological knowledge resources, word-sense-disambiguation and namedentity recognition, along with content generalization, that transforms ordinary text into a generalized form. A deep learning model of attentive encoder-decoder architecture, which is expanded to enable a coping and coverage mechanism, as well as reinforcement learning and transformer-based architectures, is trained on a generalized version of text-summary pairs, learning to predict summaries in a generalized form. The post-processing task utilizes knowledge resources, word embeddings, word-sense disambiguation and heuristic algorithms based on text similarity methods in order to transform the generalized version of a predicted summary to a final, humanreadable form. An extensive experimental procedure on three popular datasets evaluates key aspects of the proposed framework, while the obtained results exhibit promising performance, validating the robustness of the proposed approach.