CTRLsum: Towards Generic Controllable Text Summarization
Junxian He, Wojciech Kry\'sci\'nski, Bryan McCann, Nazneen Rajani,, Caiming Xiong

TL;DR
CTRLsum introduces a flexible, unified framework for controllable text summarization, allowing users to customize summaries across multiple aspects without extra training, achieving state-of-the-art results on standard datasets.
Contribution
The paper presents CTRLsum, a novel model enabling multi-aspect controllable summarization through textual prompts, without additional training or annotations.
Findings
Effective control over multiple summary aspects demonstrated.
Achieves state-of-the-art results on CNN/DailyMail.
Versatile across diverse domains and control types.
Abstract
Current summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framework for controllable summarization. Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts. Using a single unified model, CTRLsum is able to achieve a broad scope of summary manipulation at inference time without requiring additional human annotations or pre-defining a set of control aspects during training. We quantitatively demonstrate the effectiveness of our approach on three domains of summarization datasets and five control aspects: 1) entity-centric and 2) length-controllable summarization, 3) contribution summarization on scientific papers, 4) invention…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Advanced Text Analysis Techniques
