Controllable Summarization

Controllable summarization aims to generate summaries tailored to specific user needs, going beyond generic summarization methods. Current research focuses on developing models that can incorporate various control attributes, such as length, style, topic, and even the inclusion or exclusion of specific entities, often leveraging large language models and techniques like prompt tuning or reinforcement learning. This field is significant because it addresses limitations of generic summarization, enabling more effective information retrieval and personalized content creation across diverse applications, from legal document analysis to biomedical literature review.