Controllable Text Summarization (CTS) focuses on generating summaries tailored to specific user needs and criteria, addressing the limitations of generic summarization methods. ​

2 Definition and Categorization of CTS

The CTS task is defined as creating summaries that adhere to specific user-driven criteria.

• The task involves source documents and target summaries, with controllable attributes guiding the summarization process.
• Controllable attributes (CAs) include length, style, coverage, entity, structure, abstractivity, salience, role, diversity, and topic.
• Each CA is categorized based on shared characteristics and objectives, facilitating a structured approach to summarization. ​

3 Distribution of Research Focus on CAs

​Research attention on various controllable attributes is uneven, with a concentration on length, topic, and style. ​

• Length, topic, and style are the most studied attributes due to easier dataset development and broad application scenarios.
• The survey analyzes 61 research papers, categorizing CAs into 10 groups based on their characteristics and objectives. ​

4 Overview of Existing Datasets for CTS

The survey provides a comprehensive overview of datasets used in controllable summarization research. ​

• Generic datasets like CNN-DailyMail and DUC are commonly used, but they lack specific annotations for evaluating CAs.
• Derived datasets are created using heuristics from generic datasets, while human-annotated datasets provide more targeted evaluation opportunities. ​

5 Approaches to Achieve Controllable Summarization

Various methodologies are employed to achieve controllable summarization across different attributes. ​

• Length control methods include adding length in the input, in the encoder (length context vector, hyperparameter), in the decoder (embedding, parameter, positional encoding, semantic kernels), and loss/reward function.
• Style control focuses on generating user-specific summaries by adjusting tone, readability, and emotional modulation (reward function, inference style classifier, word unit prediction, gating).
• Coverage management involves regulating the granularity of information in the summary (summary sketch, event extraction).
• Entity extraction.
• Structure adding control sequence to the input; sentence beam-search; predicted argument role; prompt of entity chains.
• Abstractivity measures the degree of textual novelty between the source text and summary.
• Salience captures the most important information in a document (text-to-binary sequence learning; keywords identified by TextRank with modified attention; salience in terms of noun phrases using QA signals; two tasks of salient information identification from sentences having the highest self ROUGE score and a question generation system to generate questions whose answers are the salient sentences).
• Role oriented dialogue summarization generates summaries for different roles/agents present in a dialogue.
• Diversity compositional sampling, entity chains, beam search.
• Topic pointer-generator network topic-conditioned, external knowledge sources; aspect-specific opinions are extracted from a set of reviews by a pre-trained opinion extractor, and the summary of the opinion is generated by a generator model trained to reconstruct the reviews from the opinions; relevant sentences and keywords along with aspect tokens are fed into the pre-trained T5 model; decision-supportive summaries, an iterative algorithm that selects the sentences of the summary from a set of representative sentences.

Evaluation Metrics for Summarization

The survey discusses both automatic and human evaluation metrics used to assess the quality of generated summaries.

• Automatic metrics include n-gram-based (e.g., ROUGE, BLEU) and language-model-based evaluations using pre-trained models.
• Human evaluation assesses properties like truthfulness, relevance, fluency, and readability, often using binary or rank-based scoring mechanisms. ​

Future Directions and Research Gaps

The survey identifies limitations in current approaches and suggests potential future research trajectories.

• There is a need for more specialized datasets to evaluate specific controllable attributes effectively.
• Future research should address the challenges of generating diverse and coherent summaries while maintaining user-specific control.

Generic vs Specialized Benchmarks in CTS

The evaluation of controllable text summarization (CTS) often relies on generic news summarization datasets, which may not accurately reflect real-world applications. Specialized datasets tailored to specific controllable attributes (CAs) are essential for assessing the robustness and performance of CTS systems effectively. ​

• Over 75% of CTS works utilize or modify generic news summarization datasets.
• Only seven out of ten categories have CA-specific datasets available.
• Evaluations are often limited to specific domains, such as news, restricting the assessment of model robustness. ​

Standardization of Evaluation Metrics

The lack of standardized metrics for evaluating CA-specific summaries complicates fair comparisons across different studies. Establishing uniform evaluation metrics could enhance the assessment of CTS models. ​

• Varying metrics across studies lead to challenges in comparing models.
• Standardizing CA-specific evaluation metrics is proposed as a solution.

Importance of Explainability in CTS

Understanding the decision-making process within CTS systems is crucial for users, especially in sensitive domains like legal and medical fields. Current CTS efforts often overlook explainability, which can be improved through suitable methodologies.

• Explainability is vital for user comprehension of summary generation.
• Existing CTS efforts lack emphasis on explainability aspects.

Multilingual, Multi-modal, and Code-mixed CTS Challenges

Current research on CTS predominantly focuses on English, with limited studies in other languages or contexts. The exploration of multi-modal and multi-document settings remains largely unaddressed, presenting unique challenges and research opportunities. ​

• Only one study addresses CTS in a Japanese context.
• Multilingual and code-mixed approaches are largely unexplored.

Multi-CA Control in CTS Research

While some studies have explored multi-attribute controllable summarization, the focus has primarily been on combinations of length and entity attributes. ​ Future research should consider other important combinations of control attributes. ​

• Few works perform multi-attribute controllable summarization. ​
• There is a need for standardized multi-CA benchmarks for evaluations. ​

Reproducibility Issues in CTS Studies

A significant portion of CTS research lacks reproducibility, with 35% of studies not sharing code publicly and 25% not conducting human evaluations. ​ This hinders the scientific community's ability to validate and build upon existing work. ​

• 35% of studies do not share code. ​
• 25% did not conduct human evaluations; 79% of those did not assess Inter Annotator Agreement (IAA). ​

Leveraging Large Language Models in CTS

The rise of large language models (LLMs) offers new opportunities for enhancing CTS capabilities. ​ LLMs can be fine-tuned for context-specific nuances and serve as substitutes for human evaluators in assessing model performance. ​

• LLMs can grasp context-specific nuances without dedicated training sets. ​
• They can effectively substitute human experts for performance evaluation. ​

Comprehensive Survey on Controllable Text Summarization

This survey provides an in-depth analysis of controllable text summarization, covering various controllable attributes, existing datasets, models, limitations, and evaluation strategies. It serves as a guide for researchers interested in the field.

• The survey includes a detailed classification of controllable attributes.
• It highlights challenges and prospects for future research in CTS.

Ethical Considerations in CTS Research ​

The research adheres to ethical standards by ensuring qualitative classification and minimizing bias through independent reviews. A comprehensive set of papers is provided for public scrutiny. ​

• Each paper underwent review by at least three individuals to minimize misclassification. ​
• Ethical considerations affirm the commitment to responsible research practices. ​