Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey
This is an incremental survey that organizes existing research for researchers and practitioners in natural language processing.
The paper addresses the lack of comprehensive surveys on controllable text summarization (CTS), which tailors summaries to specific user needs, by formalizing the task, categorizing attributes, and examining datasets and methods.
Generic text summarization approaches often fail to address the specific intent and needs of individual users. Recently, scholarly attention has turned to the development of summarization methods that are more closely tailored and controlled to align with specific objectives and user needs. Despite a growing corpus of controllable summarization research, there is no comprehensive survey available that thoroughly explores the diverse controllable attributes employed in this context, delves into the associated challenges, and investigates the existing solutions. In this survey, we formalize the Controllable Text Summarization (CTS) task, categorize controllable attributes according to their shared characteristics and objectives, and present a thorough examination of existing datasets and methods within each category. Moreover, based on our findings, we uncover limitations and research gaps, while also exploring potential solutions and future directions for CTS. We release our detailed analysis of CTS papers at https://github.com/ashokurlana/controllable_text_summarization_survey.