Deep reinforcement and transfer learning for abstractive text summarization: A review
Document Type
Article
Publication Date
1-1-2022
Abstract
Automatic Text Summarization (ATS) is an important area in Natural Language Processing (NLP) with the goal of shortening a long text into a more compact version by conveying the most important points in a readable form. ATS applications continue to evolve and utilize effective approaches that are being evaluated and implemented by researchers. State-of-the-Art (SotA) technologies that demonstrate cutting-edge performance and accuracy in abstractive ATS are deep neural sequence-to-sequence models, Reinforcement Learning (RL) approaches, and Transfer Learning (TL) approaches, including Pre-Trained Language Models (PTLMs). The graph-based Transformer architecture and PTLMs have influenced tremendous advances in NLP applications. Additionally, the incorporation of recent mechanisms, such as the knowledge-enhanced mechanism, significantly enhanced the results. This study provides a comprehensive review of recent research advances in the area of abstractive text summarization for works spanning the past six years. Past and present problems are described, as well as their proposed solutions. In addition, abstractive ATS datasets and evaluation measurements are also highlighted. The paper concludes by comparing the best models and discussing future research directions.
Keywords
Abstractive summarization, Sequence-to-sequence, Reinforcement learning, Pre-trained models
Divisions
fsktm
Funders
None
Publication Title
Computer Speech & Language
Volume
71
Publisher
Academic Press Ltd- Elsevier Science Ltd
Publisher Location
24-28 OVAL RD, LONDON NW1 7DX, ENGLAND