CTRLsum: Towards Generic Controllable Text Summarization

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 purpose summarization on patent filings, and 5) question-guided summarization on news articles in a reading comprehension setting. Moreover, when used in a standard, uncontrolled summarization setting, CTRLsum achieves state-of-the-art results on the CNN/DailyMail dataset. Code and model checkpoints are available at https://github.com/salesforce/ctrl-sum