CTRLsum: Towards Generic Controllable Text Summarization (2012.04281v1)

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

• The paper introduces CTRLsum, a framework allowing controlled text summarization via control tokens without needing extra annotations.
• It leverages keywords and prompts to direct summary characteristics, achieving a 3.6-point ROUGE-2 improvement on CNN/Dailymail.
• The findings imply that user-driven control in summarization can enhance performance across diverse domains without additional training data.

Analyzing "CTRLsum: Towards Generic Controllable Text Summarization"

The paper "CTRLsum: Towards Generic Controllable Text Summarization" presents a novel framework named CTRLsum, which aims to offer controlled text summarization by allowing user manipulation across multiple aspects through textual inputs such as keywords or prompts. This work addresses a significant limitation of existing summarization models by allowing summaries to align more closely with specific user preferences.

Technical Approach and Methodology

At its core, CTRLsum leverages the strengths of abstractive summarization models while introducing a flexible control mechanism through control tokens. Control tokens are either keywords extracted from the target text or pre-defined prompts that guide the summarization in a desired direction. Notably, CTRLsum does not require additional annotations or pre-defined control codes during training, differentiating it from other approaches in the field which rely on manually obtained control annotations.

The experimental framework is comprehensive, evaluating the effectiveness of the control mechanism across several domains, including entity-centric summarization, length control, summarization in scientific domains, patent filings, and question-guided summarization in a reading comprehension context.

Numerical Results and Model Evaluation

The results presented indicate that CTRLsum achieves superior performance in controlled settings compared to state-of-the-art models, including the BART model without controls. For example, in entity-centric controllable summarization on the CNN/Dailymail dataset, the findings demonstrate a 3.6-point improvement in ROUGE-2 scores using oracle entities as control signals.

The paper provides a detailed comparison between controlled and uncontrolled performance, showing that even without interventions, the approach competes robustly with existing models. An interesting finding is the model's competence in length control, which is often challenging. CTRLsum's ability to shift summary lengths correlatively with user inputs highlights the potency of keyword-based controls in managing output characteristics seamlessly.

Theoretical and Practical Implications

The implications of such a controllable summarization framework are vast. Practically, it offers more nuanced and customized summarization solutions, which are highly beneficial for various industries and applications, including scientific research dissemination and legal text analysis. Theoretically, this framework provides an avenue to explore the dynamics of user model interactions and further develops the field of interactive machine learning.

Future Prospects

CTRLsum's design opens numerous paths for future exploration. The introduction of more granular control aspects, such as sentiment and style, and the potential integration with other NLP tasks present a multifaceted opportunity for growing this line of research. Further investigations could focus on refining the technology to ensure model robustness when scaling it to extensive and varying datasets.

In conclusion, the CTRLsum framework represents a significant advance in the domain of controllable summarization, offering a flexible and user-centric approach. It paves the way for more applied research in this area, combining user preferences with automated content generation effectively. The future work indicated by this paper holds promise for diverse, tailored applications across multiple domains, providing users with control over the generated outputs.