Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization

Panagiotis Kouris, Georgios Alexandridis, Andreas Stafylopatis


Abstract
Abstract Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches 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 deep learning predictions for generating abstractive summaries. The framework is composed 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 named entity 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, human-readable form. An extensive experimental procedure on three popular data sets evaluates key aspects of the proposed framework, while the obtained results exhibit promising performance, validating the robustness of the proposed approach.
Anthology ID:
2021.cl-4.27
Volume:
Computational Linguistics, Volume 47, Issue 4 - December 2021
Month:
December
Year:
2021
Address:
Cambridge, MA
Venue:
CL
SIG:
Publisher:
MIT Press
Note:
Pages:
813–859
Language:
URL:
https://aclanthology.org/2021.cl-4.27
DOI:
10.1162/coli_a_00417
Bibkey:
Cite (ACL):
Panagiotis Kouris, Georgios Alexandridis, and Andreas Stafylopatis. 2021. Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization. Computational Linguistics, 47(4):813–859.
Cite (Informal):
Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization (Kouris et al., CL 2021)
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PDF:
https://preview.aclanthology.org/update-css-js/2021.cl-4.27.pdf
Data
CNN/Daily MailWord Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison